ddos-attack-detection-classification.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":2929393,"sourceType":"datasetVersion","datasetId":1795814}],"dockerImageVersionId":30152,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\n\nimport matplotlib.pyplot as plt\nfrom matplotlib.pyplot import figure\nimport seaborn as sns\n\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics import classification_report\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import metrics\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn import preprocessing\n\nfrom sklearn.model_selection import cross_val_predict\nfrom sklearn.model_selection import GridSearchCV\nimport time\n\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn import svm\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.ensemble import RandomForestClassifier\n\nfrom sklearn import metrics","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:08.102790Z","iopub.execute_input":"2024-04-18T15:37:08.103173Z","iopub.status.idle":"2024-04-18T15:37:09.404131Z","shell.execute_reply.started":"2024-04-18T15:37:08.103077Z","shell.execute_reply":"2024-04-18T15:37:09.403300Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"data = pd.read_csv('../input/ddos-sdn-dataset/dataset_sdn.csv')","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:09.405671Z","iopub.execute_input":"2024-04-18T15:37:09.405903Z","iopub.status.idle":"2024-04-18T15:37:09.780346Z","shell.execute_reply.started":"2024-04-18T15:37:09.405874Z","shell.execute_reply":"2024-04-18T15:37:09.779567Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### I .Data Analysis","metadata":{}},{"cell_type":"code","source":"data.head()","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:09.781490Z","iopub.execute_input":"2024-04-18T15:37:09.781742Z","iopub.status.idle":"2024-04-18T15:37:09.816295Z","shell.execute_reply.started":"2024-04-18T15:37:09.781711Z","shell.execute_reply":"2024-04-18T15:37:09.815537Z"},"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"      dt  switch       src       dst  pktcount  bytecount  dur   dur_nsec  \\\n0  11425       1  10.0.0.1  10.0.0.8     45304   48294064  100  716000000   \n1  11605       1  10.0.0.1  10.0.0.8    126395  134737070  280  734000000   \n2  11425       1  10.0.0.2  10.0.0.8     90333   96294978  200  744000000   \n3  11425       1  10.0.0.2  10.0.0.8     90333   96294978  200  744000000   \n4  11425       1  10.0.0.2  10.0.0.8     90333   96294978  200  744000000   \n\n        tot_dur  flows  ...  pktrate  Pairflow  Protocol  port_no   tx_bytes  \\\n0  1.010000e+11      3  ...      451         0       UDP        3  143928631   \n1  2.810000e+11      2  ...      451         0       UDP        4       3842   \n2  2.010000e+11      3  ...      451         0       UDP        1       3795   \n3  2.010000e+11      3  ...      451         0       UDP        2       3688   \n4  2.010000e+11      3  ...      451         0       UDP        3       3413   \n\n  rx_bytes  tx_kbps  rx_kbps  tot_kbps  label  \n0     3917        0      0.0       0.0      0  \n1     3520        0      0.0       0.0      0  \n2     1242        0      0.0       0.0      0  \n3     1492        0      0.0       0.0      0  \n4     3665        0      0.0       0.0      0  \n\n[5 rows x 23 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>dt</th>\n      <th>switch</th>\n      <th>src</th>\n      <th>dst</th>\n      <th>pktcount</th>\n      <th>bytecount</th>\n      <th>dur</th>\n      <th>dur_nsec</th>\n      <th>tot_dur</th>\n      <th>flows</th>\n      <th>...</th>\n      <th>pktrate</th>\n      <th>Pairflow</th>\n      <th>Protocol</th>\n      <th>port_no</th>\n      <th>tx_bytes</th>\n      <th>rx_bytes</th>\n      <th>tx_kbps</th>\n      <th>rx_kbps</th>\n      <th>tot_kbps</th>\n      <th>label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>11425</td>\n      <td>1</td>\n      <td>10.0.0.1</td>\n      <td>10.0.0.8</td>\n      <td>45304</td>\n      <td>48294064</td>\n      <td>100</td>\n      <td>716000000</td>\n      <td>1.010000e+11</td>\n      <td>3</td>\n      <td>...</td>\n      <td>451</td>\n      <td>0</td>\n      <td>UDP</td>\n      <td>3</td>\n      <td>143928631</td>\n      <td>3917</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>11605</td>\n      <td>1</td>\n      <td>10.0.0.1</td>\n      <td>10.0.0.8</td>\n      <td>126395</td>\n      <td>134737070</td>\n      <td>280</td>\n      <td>734000000</td>\n      <td>2.810000e+11</td>\n      <td>2</td>\n      <td>...</td>\n      <td>451</td>\n      <td>0</td>\n      <td>UDP</td>\n      <td>4</td>\n      <td>3842</td>\n      <td>3520</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>11425</td>\n      <td>1</td>\n      <td>10.0.0.2</td>\n      <td>10.0.0.8</td>\n      <td>90333</td>\n      <td>96294978</td>\n      <td>200</td>\n      <td>744000000</td>\n      <td>2.010000e+11</td>\n      <td>3</td>\n      <td>...</td>\n      <td>451</td>\n      <td>0</td>\n      <td>UDP</td>\n      <td>1</td>\n      <td>3795</td>\n      <td>1242</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>11425</td>\n      <td>1</td>\n      <td>10.0.0.2</td>\n      <td>10.0.0.8</td>\n      <td>90333</td>\n      <td>96294978</td>\n      <td>200</td>\n      <td>744000000</td>\n      <td>2.010000e+11</td>\n      <td>3</td>\n      <td>...</td>\n      <td>451</td>\n      <td>0</td>\n      <td>UDP</td>\n      <td>2</td>\n      <td>3688</td>\n      <td>1492</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>11425</td>\n      <td>1</td>\n      <td>10.0.0.2</td>\n      <td>10.0.0.8</td>\n      <td>90333</td>\n      <td>96294978</td>\n      <td>200</td>\n      <td>744000000</td>\n      <td>2.010000e+11</td>\n      <td>3</td>\n      <td>...</td>\n      <td>451</td>\n      <td>0</td>\n      <td>UDP</td>\n      <td>3</td>\n      <td>3413</td>\n      <td>3665</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 23 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"data.shape","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:09.818143Z","iopub.execute_input":"2024-04-18T15:37:09.818394Z","iopub.status.idle":"2024-04-18T15:37:09.823944Z","shell.execute_reply.started":"2024-04-18T15:37:09.818362Z","shell.execute_reply":"2024-04-18T15:37:09.823123Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"(104345, 23)"},"metadata":{}}]},{"cell_type":"code","source":"data.info()","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:09.825145Z","iopub.execute_input":"2024-04-18T15:37:09.825380Z","iopub.status.idle":"2024-04-18T15:37:09.882809Z","shell.execute_reply.started":"2024-04-18T15:37:09.825345Z","shell.execute_reply":"2024-04-18T15:37:09.882061Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 104345 entries, 0 to 104344\nData columns (total 23 columns):\n #   Column       Non-Null Count   Dtype  \n---  ------       --------------   -----  \n 0   dt           104345 non-null  int64  \n 1   switch       104345 non-null  int64  \n 2   src          104345 non-null  object \n 3   dst          104345 non-null  object \n 4   pktcount     104345 non-null  int64  \n 5   bytecount    104345 non-null  int64  \n 6   dur          104345 non-null  int64  \n 7   dur_nsec     104345 non-null  int64  \n 8   tot_dur      104345 non-null  float64\n 9   flows        104345 non-null  int64  \n 10  packetins    104345 non-null  int64  \n 11  pktperflow   104345 non-null  int64  \n 12  byteperflow  104345 non-null  int64  \n 13  pktrate      104345 non-null  int64  \n 14  Pairflow     104345 non-null  int64  \n 15  Protocol     104345 non-null  object \n 16  port_no      104345 non-null  int64  \n 17  tx_bytes     104345 non-null  int64  \n 18  rx_bytes     104345 non-null  int64  \n 19  tx_kbps      104345 non-null  int64  \n 20  rx_kbps      103839 non-null  float64\n 21  tot_kbps     103839 non-null  float64\n 22  label        104345 non-null  int64  \ndtypes: float64(3), int64(17), object(3)\nmemory usage: 18.3+ MB\n","output_type":"stream"}]},{"cell_type":"code","source":"##### Here we see that the label contains boolean values: 0 - Benign, 1-Maliciuous \ndata.label.unique()","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:09.883873Z","iopub.execute_input":"2024-04-18T15:37:09.884086Z","iopub.status.idle":"2024-04-18T15:37:09.890404Z","shell.execute_reply.started":"2024-04-18T15:37:09.884060Z","shell.execute_reply":"2024-04-18T15:37:09.889569Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"array([0, 1])"},"metadata":{}}]},{"cell_type":"code","source":"data.label.value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:09.891348Z","iopub.execute_input":"2024-04-18T15:37:09.891564Z","iopub.status.idle":"2024-04-18T15:37:09.900888Z","shell.execute_reply.started":"2024-04-18T15:37:09.891538Z","shell.execute_reply":"2024-04-18T15:37:09.900183Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"0    63561\n1    40784\nName: label, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"label_dict = dict(data.label.value_counts())\nsns.countplot(data.label)","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:09.901810Z","iopub.execute_input":"2024-04-18T15:37:09.902059Z","iopub.status.idle":"2024-04-18T15:37:10.140717Z","shell.execute_reply.started":"2024-04-18T15:37:09.902032Z","shell.execute_reply":"2024-04-18T15:37:10.140055Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n","output_type":"stream"},{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:xlabel='label', ylabel='count'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"labels = [\"Maliciuous\",'Benign']\nsizes = [dict(data.label.value_counts())[0], dict(data.label.value_counts())[1]]\nplt.figure(figsize = (13,8))\nplt.pie(sizes, labels=labels, autopct='%1.1f%%',\n        shadow=True, startangle=90)\nplt.legend([\"Maliciuous\", \"Benign\"])\nplt.title('The percentage of Benign and Maliciuos Requests in dataset')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:10.142174Z","iopub.execute_input":"2024-04-18T15:37:10.142453Z","iopub.status.idle":"2024-04-18T15:37:10.389483Z","shell.execute_reply.started":"2024-04-18T15:37:10.142414Z","shell.execute_reply":"2024-04-18T15:37:10.388682Z"},"trusted":true},"execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 936x576 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"data.describe()","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:10.392171Z","iopub.execute_input":"2024-04-18T15:37:10.392454Z","iopub.status.idle":"2024-04-18T15:37:10.507139Z","shell.execute_reply.started":"2024-04-18T15:37:10.392422Z","shell.execute_reply":"2024-04-18T15:37:10.506272Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"                  dt         switch       pktcount     bytecount  \\\ncount  104345.000000  104345.000000  104345.000000  1.043450e+05   \nmean    17927.514169       4.214260   52860.954746  3.818660e+07   \nstd     11977.642655       1.956327   52023.241460  4.877748e+07   \nmin      2488.000000       1.000000       0.000000  0.000000e+00   \n25%      7098.000000       3.000000     808.000000  7.957600e+04   \n50%     11905.000000       4.000000   42828.000000  6.471930e+06   \n75%     29952.000000       5.000000   94796.000000  7.620354e+07   \nmax     42935.000000      10.000000  260006.000000  1.471280e+08   \n\n                 dur      dur_nsec       tot_dur          flows  \\\ncount  104345.000000  1.043450e+05  1.043450e+05  104345.000000   \nmean      321.497398  4.613880e+08  3.218865e+11       5.654234   \nstd       283.518232  2.770019e+08  2.834029e+11       2.950036   \nmin         0.000000  0.000000e+00  0.000000e+00       2.000000   \n25%       127.000000  2.340000e+08  1.270000e+11       3.000000   \n50%       251.000000  4.180000e+08  2.520000e+11       5.000000   \n75%       412.000000  7.030000e+08  4.130000e+11       7.000000   \nmax      1881.000000  9.990000e+08  1.880000e+12      17.000000   \n\n           packetins     pktperflow   byteperflow        pktrate  \\\ncount  104345.000000  104345.000000  1.043450e+05  104345.000000   \nmean     5200.383468    6381.715291  4.716150e+06     212.210676   \nstd      5257.001450    7404.777808  7.560116e+06     246.855123   \nmin         4.000000 -130933.000000 -1.464426e+08   -4365.000000   \n25%      1943.000000      29.000000  2.842000e+03       0.000000   \n50%      3024.000000    8305.000000  5.521680e+05     276.000000   \n75%      7462.000000   10017.000000  9.728112e+06     333.000000   \nmax     25224.000000   19190.000000  1.495387e+07     639.000000   \n\n            Pairflow        port_no      tx_bytes      rx_bytes  \\\ncount  104345.000000  104345.000000  1.043450e+05  1.043450e+05   \nmean        0.600987       2.331094  9.325264e+07  9.328039e+07   \nstd         0.489698       1.084333  1.519380e+08  1.330004e+08   \nmin         0.000000       1.000000  2.527000e+03  8.560000e+02   \n25%         0.000000       1.000000  4.743000e+03  3.539000e+03   \n50%         1.000000       2.000000  4.219610e+06  1.338339e+07   \n75%         1.000000       3.000000  1.356398e+08  1.439277e+08   \nmax         1.000000       5.000000  1.269982e+09  9.905962e+08   \n\n             tx_kbps        rx_kbps       tot_kbps          label  \ncount  104345.000000  103839.000000  103839.000000  104345.000000  \nmean      998.899756    1003.811420    2007.578742       0.390857  \nstd      2423.471618    2054.887034    3144.437173       0.487945  \nmin         0.000000       0.000000       0.000000       0.000000  \n25%         0.000000       0.000000       0.000000       0.000000  \n50%         0.000000       0.000000       4.000000       0.000000  \n75%       251.000000     557.000000    3838.000000       1.000000  \nmax     20580.000000   16577.000000   20580.000000       1.000000  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>dt</th>\n      <th>switch</th>\n      <th>pktcount</th>\n      <th>bytecount</th>\n      <th>dur</th>\n      <th>dur_nsec</th>\n      <th>tot_dur</th>\n      <th>flows</th>\n      <th>packetins</th>\n      <th>pktperflow</th>\n      <th>byteperflow</th>\n      <th>pktrate</th>\n      <th>Pairflow</th>\n      <th>port_no</th>\n      <th>tx_bytes</th>\n      <th>rx_bytes</th>\n      <th>tx_kbps</th>\n      <th>rx_kbps</th>\n      <th>tot_kbps</th>\n      <th>label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>104345.000000</td>\n      <td>104345.000000</td>\n      <td>104345.000000</td>\n      <td>1.043450e+05</td>\n      <td>104345.000000</td>\n      <td>1.043450e+05</td>\n      <td>1.043450e+05</td>\n      <td>104345.000000</td>\n      <td>104345.000000</td>\n      <td>104345.000000</td>\n      <td>1.043450e+05</td>\n      <td>104345.000000</td>\n      <td>104345.000000</td>\n      <td>104345.000000</td>\n      <td>1.043450e+05</td>\n      <td>1.043450e+05</td>\n      <td>104345.000000</td>\n      <td>103839.000000</td>\n      <td>103839.000000</td>\n      <td>104345.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>17927.514169</td>\n      <td>4.214260</td>\n      <td>52860.954746</td>\n      <td>3.818660e+07</td>\n      <td>321.497398</td>\n      <td>4.613880e+08</td>\n      <td>3.218865e+11</td>\n      <td>5.654234</td>\n      <td>5200.383468</td>\n      <td>6381.715291</td>\n      <td>4.716150e+06</td>\n      <td>212.210676</td>\n      <td>0.600987</td>\n      <td>2.331094</td>\n      <td>9.325264e+07</td>\n      <td>9.328039e+07</td>\n      <td>998.899756</td>\n      <td>1003.811420</td>\n      <td>2007.578742</td>\n      <td>0.390857</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>11977.642655</td>\n      <td>1.956327</td>\n      <td>52023.241460</td>\n      <td>4.877748e+07</td>\n      <td>283.518232</td>\n      <td>2.770019e+08</td>\n      <td>2.834029e+11</td>\n      <td>2.950036</td>\n      <td>5257.001450</td>\n      <td>7404.777808</td>\n      <td>7.560116e+06</td>\n      <td>246.855123</td>\n      <td>0.489698</td>\n      <td>1.084333</td>\n      <td>1.519380e+08</td>\n      <td>1.330004e+08</td>\n      <td>2423.471618</td>\n      <td>2054.887034</td>\n      <td>3144.437173</td>\n      <td>0.487945</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>2488.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>2.000000</td>\n      <td>4.000000</td>\n      <td>-130933.000000</td>\n      <td>-1.464426e+08</td>\n      <td>-4365.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>2.527000e+03</td>\n      <td>8.560000e+02</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>7098.000000</td>\n      <td>3.000000</td>\n      <td>808.000000</td>\n      <td>7.957600e+04</td>\n      <td>127.000000</td>\n      <td>2.340000e+08</td>\n      <td>1.270000e+11</td>\n      <td>3.000000</td>\n      <td>1943.000000</td>\n      <td>29.000000</td>\n      <td>2.842000e+03</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>4.743000e+03</td>\n      <td>3.539000e+03</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>11905.000000</td>\n      <td>4.000000</td>\n      <td>42828.000000</td>\n      <td>6.471930e+06</td>\n      <td>251.000000</td>\n      <td>4.180000e+08</td>\n      <td>2.520000e+11</td>\n      <td>5.000000</td>\n      <td>3024.000000</td>\n      <td>8305.000000</td>\n      <td>5.521680e+05</td>\n      <td>276.000000</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n      <td>4.219610e+06</td>\n      <td>1.338339e+07</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>4.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>29952.000000</td>\n      <td>5.000000</td>\n      <td>94796.000000</td>\n      <td>7.620354e+07</td>\n      <td>412.000000</td>\n      <td>7.030000e+08</td>\n      <td>4.130000e+11</td>\n      <td>7.000000</td>\n      <td>7462.000000</td>\n      <td>10017.000000</td>\n      <td>9.728112e+06</td>\n      <td>333.000000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>1.356398e+08</td>\n      <td>1.439277e+08</td>\n      <td>251.000000</td>\n      <td>557.000000</td>\n      <td>3838.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>42935.000000</td>\n      <td>10.000000</td>\n      <td>260006.000000</td>\n      <td>1.471280e+08</td>\n      <td>1881.000000</td>\n      <td>9.990000e+08</td>\n      <td>1.880000e+12</td>\n      <td>17.000000</td>\n      <td>25224.000000</td>\n      <td>19190.000000</td>\n      <td>1.495387e+07</td>\n      <td>639.000000</td>\n      <td>1.000000</td>\n      <td>5.000000</td>\n      <td>1.269982e+09</td>\n      <td>9.905962e+08</td>\n      <td>20580.000000</td>\n      <td>16577.000000</td>\n      <td>20580.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# Let's look at the vizualisation of Null valued features\nfigure(figsize=(9, 5), dpi=80)\ndata[data.columns[data.isna().sum() >= 0]].isna().sum().sort_values().plot.bar()\nplt.title(\"Features which has NuLL values\")","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:10.508540Z","iopub.execute_input":"2024-04-18T15:37:10.509169Z","iopub.status.idle":"2024-04-18T15:37:10.971664Z","shell.execute_reply.started":"2024-04-18T15:37:10.509120Z","shell.execute_reply":"2024-04-18T15:37:10.970891Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, 'Features which has NuLL values')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 720x400 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"data.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:10.973082Z","iopub.execute_input":"2024-04-18T15:37:10.973391Z","iopub.status.idle":"2024-04-18T15:37:11.017402Z","shell.execute_reply.started":"2024-04-18T15:37:10.973351Z","shell.execute_reply":"2024-04-18T15:37:11.016707Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"dt               0\nswitch           0\nsrc              0\ndst              0\npktcount         0\nbytecount        0\ndur              0\ndur_nsec         0\ntot_dur          0\nflows            0\npacketins        0\npktperflow       0\nbyteperflow      0\npktrate          0\nPairflow         0\nProtocol         0\nport_no          0\ntx_bytes         0\nrx_bytes         0\ntx_kbps          0\nrx_kbps        506\ntot_kbps       506\nlabel            0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"#### Let's support which columns NUMERIC and which is OBJECT\n\nnumeric_df = data.select_dtypes(include=['int64', 'float64'])\nobject_df = data.select_dtypes(include=['object'])\nnumeric_cols = numeric_df.columns\nobject_cols = object_df.columns\nprint('Numeric Columns: ')\nprint(numeric_cols, '\\n')\nprint('Object Columns: ')\nprint(object_cols, '\\n')\nprint('Number of Numeric Features: ', len(numeric_cols))\nprint('Number of Object Features: ', len(object_cols))","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:11.018531Z","iopub.execute_input":"2024-04-18T15:37:11.018832Z","iopub.status.idle":"2024-04-18T15:37:11.037244Z","shell.execute_reply.started":"2024-04-18T15:37:11.018790Z","shell.execute_reply":"2024-04-18T15:37:11.036313Z"},"trusted":true},"execution_count":13,"outputs":[{"name":"stdout","text":"Numeric Columns: \nIndex(['dt', 'switch', 'pktcount', 'bytecount', 'dur', 'dur_nsec', 'tot_dur',\n       'flows', 'packetins', 'pktperflow', 'byteperflow', 'pktrate',\n       'Pairflow', 'port_no', 'tx_bytes', 'rx_bytes', 'tx_kbps', 'rx_kbps',\n       'tot_kbps', 'label'],\n      dtype='object') \n\nObject Columns: \nIndex(['src', 'dst', 'Protocol'], dtype='object') \n\nNumber of Numeric Features:  20\nNumber of Object Features:  3\n","output_type":"stream"}]},{"cell_type":"code","source":"object_df.head()","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:11.038530Z","iopub.execute_input":"2024-04-18T15:37:11.038818Z","iopub.status.idle":"2024-04-18T15:37:11.056064Z","shell.execute_reply.started":"2024-04-18T15:37:11.038777Z","shell.execute_reply":"2024-04-18T15:37:11.055283Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"        src       dst Protocol\n0  10.0.0.1  10.0.0.8      UDP\n1  10.0.0.1  10.0.0.8      UDP\n2  10.0.0.2  10.0.0.8      UDP\n3  10.0.0.2  10.0.0.8      UDP\n4  10.0.0.2  10.0.0.8      UDP","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>src</th>\n      <th>dst</th>\n      <th>Protocol</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>10.0.0.1</td>\n      <td>10.0.0.8</td>\n      <td>UDP</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>10.0.0.1</td>\n      <td>10.0.0.8</td>\n      <td>UDP</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>10.0.0.2</td>\n      <td>10.0.0.8</td>\n      <td>UDP</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>10.0.0.2</td>\n      <td>10.0.0.8</td>\n      <td>UDP</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>10.0.0.2</td>\n      <td>10.0.0.8</td>\n      <td>UDP</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"#### Let's look at Oblect columns (Source Destination Protocol)\n\nfigure(figsize=(12, 7), dpi=80)\nplt.barh(list(dict(data.src.value_counts()).keys()), dict(data.src.value_counts()).values(), color='lawngreen')\n\nfor idx, val in enumerate(dict(data.src.value_counts()).values()):\n    plt.text(x = val, y = idx-0.2, s = str(val), color='r', size = 13)\n\nplt.xlabel('Number of Requests')\nplt.ylabel('IP addres of sender')\nplt.title('Number of all reqests')","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:11.057186Z","iopub.execute_input":"2024-04-18T15:37:11.057733Z","iopub.status.idle":"2024-04-18T15:37:11.521236Z","shell.execute_reply.started":"2024-04-18T15:37:11.057690Z","shell.execute_reply":"2024-04-18T15:37:11.520471Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, 'Number of all reqests')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 960x560 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"figure(figsize=(12, 7), dpi=80)\nplt.barh(list(dict(data[data.label == 1].src.value_counts()).keys()), dict(data[data.label == 1].src.value_counts()).values(), color='blue')\n\nfor idx, val in enumerate(dict(data[data.label == 1].src.value_counts()).values()):\n    plt.text(x = val, y = idx-0.2, s = str(val), color='r', size = 13)\n\nplt.xlabel('Number of Requests')\nplt.ylabel('IP addres of sender')\nplt.title('Number of Attack requests')","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:11.522447Z","iopub.execute_input":"2024-04-18T15:37:11.522657Z","iopub.status.idle":"2024-04-18T15:37:11.979571Z","shell.execute_reply.started":"2024-04-18T15:37:11.522631Z","shell.execute_reply":"2024-04-18T15:37:11.978867Z"},"trusted":true},"execution_count":16,"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, 'Number of Attack requests')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 960x560 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"figure(figsize=(12, 7), dpi=80)\nplt.barh(list(dict(data.src.value_counts()).keys()), dict(data.src.value_counts()).values(), color='lawngreen')\nplt.barh(list(dict(data[data.label == 1].src.value_counts()).keys()), dict(data[data.label == 1].src.value_counts()).values(), color='blue')\n\nfor idx, val in enumerate(dict(data.src.value_counts()).values()):\n    plt.text(x = val, y = idx-0.2, s = str(val), color='r', size = 13)\n\nfor idx, val in enumerate(dict(data[data.label == 1].src.value_counts()).values()):\n    plt.text(x = val, y = idx-0.2, s = str(val), color='w', size = 13)\n\n\nplt.xlabel('Number of Requests')\nplt.ylabel('IP addres of sender')\nplt.legend(['All','malicious'])\nplt.title('Number of requests from different IP adress')","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:11.980902Z","iopub.execute_input":"2024-04-18T15:37:11.981210Z","iopub.status.idle":"2024-04-18T15:37:12.845523Z","shell.execute_reply.started":"2024-04-18T15:37:11.981172Z","shell.execute_reply":"2024-04-18T15:37:12.844767Z"},"trusted":true},"execution_count":17,"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, 'Number of requests from different IP adress')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 960x560 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"figure(figsize=(10, 6), dpi=80)\nplt.bar(list(dict(data.Protocol.value_counts()).keys()), dict(data.Protocol.value_counts()).values(), color='r')\nplt.bar(list(dict(data[data.label == 1].Protocol.value_counts()).keys()), dict(data[data.label == 1].Protocol.value_counts()).values(), color='b')\n\nplt.text(x = 0 - 0.15, y = 41321 + 200, s = str(41321), color='black', size=17)\nplt.text(x = 1 - 0.15, y = 33588 + 200, s = str(33588), color='black', size=17)\nplt.text(x = 2 - 0.15, y = 29436 + 200, s = str(29436), color='black', size=17)\n\nplt.text(x = 0 - 0.15, y = 9419 + 200, s = str(9419), color='w', size=17)\nplt.text(x = 1 - 0.15, y = 17499 + 200, s = str(17499), color='w', size=17)\nplt.text(x = 2 - 0.15, y = 13866 + 200, s = str(13866), color='w', size=17)\n\nplt.xlabel('Protocol')\nplt.ylabel('Count')\nplt.legend(['All', 'malicious'])\nplt.title('The number of requests from different protocols')","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:12.846605Z","iopub.execute_input":"2024-04-18T15:37:12.846801Z","iopub.status.idle":"2024-04-18T15:37:13.202437Z","shell.execute_reply.started":"2024-04-18T15:37:12.846775Z","shell.execute_reply":"2024-04-18T15:37:13.201704Z"},"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, 'The number of requests from different protocols')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x480 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"df = data.copy()","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:13.203420Z","iopub.execute_input":"2024-04-18T15:37:13.203607Z","iopub.status.idle":"2024-04-18T15:37:13.217001Z","shell.execute_reply.started":"2024-04-18T15:37:13.203583Z","shell.execute_reply":"2024-04-18T15:37:13.216298Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"code","source":"figure(figsize=(8, 4), dpi=80)\nplt.hist(df.dur, bins=20, color='b')\nplt.title('Duration')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:13.218100Z","iopub.execute_input":"2024-04-18T15:37:13.218315Z","iopub.status.idle":"2024-04-18T15:37:13.492405Z","shell.execute_reply.started":"2024-04-18T15:37:13.218288Z","shell.execute_reply":"2024-04-18T15:37:13.491590Z"},"trusted":true},"execution_count":20,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x320 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAioAAAEkCAYAAAAB0hpeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAxOAAAMTgF/d4wjAAAXwElEQVR4nO3df4xdZ53f8fen69SgDBuIZcc0N1ZwHZKKNlgUCxWxyf4oK1gqisgqUaSgbpRKpNKq2VhRsyBV2j/6w2xFoChaJeGHQtgiooBItASyyy4k0KbZZgtOnd2C7RJjj/MDEjYNsywk03z7xz0Dl8nM+M7MvZ5n7n2/pKPr+3zPj+fxM8f55Nxz7qSqkCRJatHf2egOSJIkLcegIkmSmmVQkSRJzTKoSJKkZhlUJElSswwqkiSpWQYVSZLULIOKpImR5EtJ/u1G90PS6MQvfJO0FknuB94M/AR4EXgO+J/AR6vq3jEf+3zgMeCCqjo6zmNJ2lheUZG0Hr9fVa+oqrOAfwz8CfCZJP9+rTtMcsbIeidp0zOoSBqJqvpeVf0B8DvA7ybZk+T2JH84uF6S+5P8u4H3leT6JA8m+RvgsiT/MMmfJfl+kv+b5M+T/OrAbv6ye30kyVySW5bZ9z/oPg56OslskluTnLWoL/85yae745xI8q9G/7cjaa0MKpJG7dPd66+tYpv3Av8SmAHu6doOALuAHcCXgM8n2dHVXte9vr6qZqrq2sU7TPIK4E+Bv+r2sw+4CPjkolV/C/gY8Cr6IevmJHtW0XdJY2RQkTRSVfW3wNPAtlVs9qGq+qvq+9uqerSqvtz9+SdV9XtAAW9axT7/GfB3gRur6kdV9QT9IPLPk+wcWO9zVfWVqnqxqj4H/ID+x1iSGmBQkTRSSV4ObAeeWcVmjy3ax64kn0lyPMlzSZ4FfpH+1ZVhnQd8t6rmB9oWbrzdNdD2+KLt/gZ4xSqOI2mMDCqSRu1K+lc/vgL8EDhzUf3vLbHNi4vef5T+v0/7quoX6X8s8xyQZdZfyglgV5ItA21/v3s9PsT2khpgUJE0Ekm2J3kv8GHgP1XVEeAvgF9JclGSM5L8DvCaIXZ3FjAH/HWSM4H/SP/+lQXfpx9WLlxhH/cC88B/SPLy7uOeDwF/VFVPrm50kjaKQUXSevyb7qmb54BvAr8BXFVVv9vV/wvwGeBB+lc4Xgn8tyH2+6+B1wN/Tf9m2JPA7EKxuw/m/cDHkjyb5A8W76CqngPe2u1nlv53vBwF/sXqhylpo/iFb5IkqVleUZEkSc0yqEiSpGYZVCRJUrMMKpIkqVkGFUmS1CyDiiRJataWU6+yeWzdurW2b9++0d2QJEmrcPLkyeerautStYkKKtu3b2d2dvbUK0qSpGYk+f5yNT/6kSRJzTKoSJKkZhlUJElSswwqkiSpWQYVSZLUrKGCSpKXJbk7yeEkjyT5cpI9XW1HkvuSHEnyaJJLBrYbeU2SJE2P1VxRuQ24sKpeD9wDfKxrPwA8VFUXAFcDn05yxhhrkiRpSgwVVKrqx1X1xaqqrukh4Pzuz5cDt3TrPQw8Dlw6xpokSZoSa71H5TrgniTbgDOq6smB2jFg1zhqa+yrJEnapFb9zbRJ3g/sAX4NePnIe7S6vuwH9i+8P+usszawN+1JxrPfn15XkyRpzFZ1RSXJDcC7gbdX1Y+q6hlgPsnOgdXOB46Po7a4P1V1U1X1FpaZmZnVDEeSJDVu6KDSXb24EnhrVT07ULoLuLZbZx9wLvDAGGunXTK+RZIkLW+oj36S9IAPAt8Bvpr+f2F/UlVvAm4EPpXkCPA8cFVVvdBtOo6aJEmaEqkJuuGg1+vVOH578jivfIzzr997VCRJm0GSk1XVW6rmN9NKkqRmGVQkSVKzDCqSJKlZBhVJktQsg4okSWqWQUWSJDXLoCJJkpq16t/1o9Hy22klSVqeV1QkSVKzDCqSJKlZBhVJktQsg4okSWqWQUWSJDXLoCJJkpplUJEkSc0yqEiSpGYZVCRJUrMMKpIkqVkGFUmS1CyDiiRJatZQQSXJR5IcS1JJ9nZt25IcHFgOJ5lPcnZXvz/JYwP16wf2tyPJfUmOJHk0ySXD1CRJ0nQZ9rcnfxb4feC/LjRU1TPA3oX3SW4ALq2qHwxsd31V3b3E/g4AD1XV25LsAz6f5DVV9cIpapIkaYoMdUWlqr5WVbOnWO0a4ONDHvdy4JZu3w8DjwOXDlGTJElTZCT3qCR5M/Aq4AuLSgeSHEpyZ5Ld3brbgDOq6smB9Y4Bu1aqjaKfkiRpcxnVzbTXAHdU1fxA23uq6iLgYuDrvDTErFuS/UlmF5a5ublRH0KSJG2gdQeVJDP0P675xGB7VZ3oXquqbgZ2J9nW3dsyn2TnwOrnA8dXqi117Kq6qap6C8vMzMx6hyNJkhoyiisqVwCPVNW3FhqSbElyzsD7y4CnuiACcBdwbVfbB5wLPDBETZIkTZGhnvpJcivwDmAn8MdJflhVe7ryNcBHF22yFbg3yVbgReBp4J0D9RuBTyU5AjwPXDXwVM9KNUmSNEVSVRvdh5Hp9Xo1O3uqh5NWLxn5Lje1CfqRkSQ1IMnJquotVfObaSVJUrMMKpIkqVkGFUmS1CyDiiRJapZBRZIkNcugIkmSmmVQkSRJzTKoSJKkZhlUJElSswwqkiSpWQYVSZLULIOKJElqlkFFkiQ1y6AiSZKaZVCRJEnNMqhIkqRmGVQkSVKzDCqSJKlZBhVJktSsoYJKko8kOZakkuwdaD+W5NtJDnbLFQO1C5I8mORwkoeTvG69NUmSNF2GvaLyWeAtwHeXqF1RVXu75c6B9luB26rqtcAHgNtHUJMkSVNkqKBSVV+rqtlhd5pkB/BG4A+7ps8B5yXZs9basMeWJEmTYxT3qNyR5FCSjyfZ3rWdBzxRVfMAVVXAcWDXOmqSJGnKrDeoXFJVFwNvAJ4GPrn+Lg0vyf4kswvL3Nzc6Ty8JEkasy3r2biqjnevLyT5MHC4K50AXp1kS1XNJwn9qyLHgefWWFvq+DcBNy287/V6tZ7xaDjJ+PZdzqAkacCar6gkOTPJKweargS+CVBV3wO+AVzV1S4DZqvq6Fpra+2nJEnavFJD/C9skluBdwA7gWeAHwK/Tv9m118AAnwHuK6qjnXbXEj/iZ1t9K+UXF1Vh9ZTO5Ver1ezs0Pf8zu0cV5B0M/ziookTZ8kJ6uqt2RtmKCyWRhUNr8J+nGUJA1ppaDiN9NKkqRmGVQkSVKzDCqSJKlZBhVJktQsg4okSWqWQUWSJDXLoCJJkpplUJEkSc0yqEiSpGYZVCRJUrMMKpIkqVkGFUmS1CyDiiRJapZBRZIkNcugIkmSmmVQkSRJzTKoSJKkZhlUJElSswwqkiSpWQYVSZLUrKGCSpKPJDmWpJLs7dpeluTuJIeTPJLky0n2DGxzf5LHkhzslusHajuS3JfkSJJHk1wyTE2SJE2XYa+ofBZ4C/DdRe23ARdW1euBe4CPLapfX1V7u+VDA+0HgIeq6gLgauDTSc4YoiZJkqbIUEGlqr5WVbOL2n5cVV+squqaHgLOH/K4lwO3dPt5GHgcuHSImiRJmiKjvEflOvpXVQYdSHIoyZ1JdgMk2QacUVVPDqx3DNi1Um2E/ZQkSZvESIJKkvcDe4D3DTS/p6ouAi4Gvg58YRTHWnTc/UlmF5a5ublRH0KSJG2gdQeVJDcA7wbeXlU/WmivqhPda1XVzcDuJNuq6hlgPsnOgd2cDxxfqbbUsavqpqrqLSwzMzPrHY4kSWrIuoJKkv3AlcBbq+rZgfYtSc4ZeH8Z8FQXRADuAq7tavuAc4EHhqhJkqQpsmWYlZLcCrwD2An8cZIfAr8MfBD4DvDVJAA/qao3AVuBe5NsBV4EngbeObDLG4FPJTkCPA9cVVUvDFGTJElTJD97aGfz6/V6NTs7e+oVV6mfwXQ6TNCPoyRpSElOVlVvqZrfTCtJkpplUJEkSc0yqEiSpGYZVCRJUrMMKpIkqVkGFUmS1CyDiiRJapZBRZIkNcugIkmSmmVQkSRJzTKoSJKkZhlUJElSswwqkiSpWQYVSZLULIOKJElq1paN7oA0KBnPfqvGs19J0nh5RUWSJDXLoCJJkpplUJEkSc0yqEiSpGYNFVSSfCTJsSSVZO9A+wVJHkxyOMnDSV43zpokSZouw15R+SzwFuC7i9pvBW6rqtcCHwBuH3NNkiRNkdQqnttMcgx4V1UdTLIDOAqcXVXzSQI8QT/QPDfqWlUdPVX/er1ezc7ODj2eYY3rkVmdPj6eLEntSnKyqnpL1dZzj8p5wBNVNQ9Q/cRzHNg1ppokSZoym/pm2iT7k8wuLHNzcxvdJUmSNELrCSongFcn2QLQfUyzi/4VkHHUXqKqbqqq3sIyMzOzjuFIkqTWrDmoVNX3gG8AV3VNlwGzVXV0HLW19lOSJG1eQ91Mm+RW4B3ATuAZ4IdVtSfJhfSfytlG/0bYq6vqULfNyGun4s20Wo4300pSu1a6mXZVT/20zqCi5UzQj7kkTZxxPfUjSZI0VgYVSZLULIOKJElqlkFFkiQ1y6AiSZKaZVCRJEnNMqhIkqRmGVQkSVKzDCqSJKlZBhVJktQsg4okSWqWQUWSJDXLoCJJkpplUJEkSc0yqEiSpGYZVCRJUrMMKpIkqVkGFUmS1CyDiiRJapZBRZIkNWvdQSXJtiQHB5bDSeaTnJ3k/iSPDdSuH9huR5L7khxJ8miSS4apSZKk6bFlvTuoqmeAvQvvk9wAXFpVP0gCcH1V3b3EpgeAh6rqbUn2AZ9P8pqqeuEUNUmSNCXG8dHPNcDHh1jvcuAWgKp6GHgcuHSImiRJmhIjDSpJ3gy8CvjCQPOBJIeS3Jlkd7feNuCMqnpyYL1jwK6VaqPsqyRJat+or6hcA9xRVfPd+/dU1UXAxcDX+fkAs25J9ieZXVjm5uZGuXtJkrTBRhZUkszQ/8jmEwttVXWie62quhnYnWRbd1/LfJKdA7s4Hzi+Um3xMavqpqrqLSwzMzOjGo4kSWrAKK+oXAE8UlXfAkiyJck5C8UklwFPdUEE4C7g2q62DzgXeGCImiRJmhLrfupnwDXARwfebwXuTbIVeBF4GnjnQP1G4FNJjgDPA1cNPNWzUk2SJE2JVNVG92Fker1ezc7Ojny//aestZlN0I+5JE2cJCerqrdUzW+mlSRJzTKoSJKkZhlUJElSswwqkiSpWQYVSZLULIOKJElqlkFFkiQ1y6AiSZKaZVCRJEnNMqhIkqRmGVQkSVKzDCqSJKlZBhVJktQsg4okSWqWQUWSJDXLoCJJkpplUJEkSc0yqEiSpGYZVCRJUrMMKpIkqVkjCSpJjiX5dpKD3XJF135BkgeTHE7ycJLXDWyzppokSZoeo7yickVV7e2WO7u2W4Hbquq1wAeA2wfWX2tNkiRNiVTV+neSHAPeVVUHB9p2AEeBs6tqPkmAJ4C3AM+tpVZVR1fqR6/Xq9nZ2XWP56XjG/kudZqN4MdckjQmSU5WVW+p2iivqNyR5FCSjyfZDpwHPFFV8wDVT0THgV3rqEmSpCkyqqBySVVdDLwBeBr45Ij2u6Ik+5PMLixzc3On47CSJOk0GUlQqarj3esLwIeBXwJOAK9OsgWg+whnF/2rI2utLT7uTVXVW1hmZmZGMRxJktSIdQeVJGcmeeVA05XAN6vqe8A3gKu69suA2ao6utbaevsqSZI2l3XfTJtkN/A54BeAAN8BrquqY0kupP/Ezjb6N8leXVWHuu3WVFuJN9NqOd5MK0ntWulm2pE89dMKg4qWM0E/5pI0cU7XUz+SJEkjZVCRJEnNMqhIkqRmGVQkSVKzDCqSJKlZWza6A9LpMM4nt3yiSJLGxysqkiSpWQYVSZLULIOKJElqlkFFkiQ1y6AiSZKaZVCRJEnNMqhIkqRmGVQkSVKzDCqSJKlZBhVJktQsg4okSWqWQUWSJDXLoCJJkpq17qCS5GVJ7k5yOMkjSb6cZE9Xuz/JY0kOdsv1A9vtSHJfkiNJHk1yyTA1qTXJ5lskabPYMqL93AZ8qaoqyW8DHwN+uatdX1V3L7HNAeChqnpbkn3A55O8pqpeOEVN0hQaV8CqGs9+JY3Guq+oVNWPq+qLVT893R8Czh9i08uBW7p9PAw8Dlw6RE2SJE2Jcdyjch1wz8D7A0kOJbkzyW6AJNuAM6rqyYH1jgG7VqqNoa/SVNroj578uErSsEYaVJK8H9gDvK9rek9VXQRcDHwd+MKIj7c/yezCMjc3N8rdS5KkDTayoJLkBuDdwNur6kcAVXWie62quhnYnWRbVT0DzCfZObCL84HjK9UWH7Oqbqqq3sIyMzMzquFIkqQGjCSoJNkPXAm8taqe7dq2JDlnYJ3LgKe6IAJwF3BtV9sHnAs8MERNkiRNiXU/9ZOkB3wQ+A7w1fQ/TP4J8KvAvUm2Ai8CTwPvHNj0RuBTSY4AzwNXDTzVs1JNkiRNidQEPZvX6/VqdnZ25Pv1Rj5pck3QP4HSppXkZFX1lqr5zbSSJKlZBhVJktQsg4okSWqWQUWSJDXLoCJJkpplUJEkSc0yqEiSpGYZVCRJUrMMKpIkqVkGFUmS1CyDiiRJapZBRZIkNcugIkmSmmVQkSRJzTKoSJKkZhlUJElSswwqkiSpWVs2ugOStJGS8e27anz7lqaFV1QkSVKzDCqSJKlZzQaVJBckeTDJ4SQPJ3ndRvdJkiSdXs0GFeBW4Laqei3wAeD2je2OJEk63Zq8mTbJDuCNwK93TZ8Dbk6yp6qOblzPJGl447xRVzrdNurm8FavqJwHPFFV8wBVVcBxYNeG9kqSJJ1WTV5RGVaS/cD+gab/l+TJMRxqBpgbw35b5Xgnm+OdbI53sm3YeMd8hXD7ssetBh/07z76OQqcXVXzSQI8AbxlIz76STJbVb3TfdyN4ngnm+OdbI53sk3beKHRj36q6nvAN4CruqbLgFnvT5Ekabq0/NHPe4Hbk7wfeA64eoP7I0mSTrNmg0pVfRv4Jxvdj85NG92B08zxTjbHO9kc72SbtvG2eY+KJEkSNHqPiiRJEhhUJElSwwwqpzBpv3MoycuS3N2N55EkX06yp6vdn+SxJAe75fqB7XYkuS/JkSSPJrlk40axOkmOJfn2wLiu6NqXndvNOu9Jtg2M82DX//kkZ0/C/Cb5SDeflWTvQPua5rL1eV5qvCudw119087zCvO75Dnc1SZtfpc9h7v6pp3fNasqlxUW4CvAb3V//k3g4Y3u0zrH8zLgN/jZ/Um/Ddzf/fl+4F3LbPcJ4Pe6P+8DZoEzNno8Q475GLB3NXM7KfMO3AD80aTML3AJ0Fs8p2udy9bneanxrnQOb/Z5XmF+lzyHJ3F+l1jnp+fwZp/fNf89bXQHWl6AHfQfjd7SvQ/wJLBno/s2wjG+ETjW/XmlE2AO2Dnw/n8A/3Sj+z/kGF/yj8BKcztJ8w7874U5naT5XfQf7jXN5Waa51P8h+yn5/CkzPPi8S43/imZ35+ew5Myv6td/OhnZdPwO4euA+4ZeH8gyaEkdybZDf1LkfRT+eCvJzjG5vp7uKMb18eTbGfluZ2IeU/yZuBVwBcGmidxftc6lxMxz7z0HIbJnOfF5zBM+Pwucw7DZM7vsgwqUyz9L9PbA7yva3pPVV0EXAx8nZeeHJvVJVV1MfAG4Gngkxvcn9PlGuCOhX+omdz5nVpLnMMwmfPsOfwzkzi/KzKorOwE8OokWwCShH46Pb6hvRqBJDcA7wbeXlU/AqiqE91rVdXNwO4k26rqGWA+yc6BXZzPJvl7qKrj3esLwIeBX2Llud30855kBric/mfWwOTOL2ufy009z0udwzCZ87zMOQyTPb8vOYdhMuf3VAwqK6gJ/Z1D6f/W6SuBt1bVs13bliTnDKxzGfBU98MPcBdwbVfbB5wLPHA6+70WSc5M8sqBpiuBb640txMy71cAj1TVt2By5xdWPk8ndZ6XOoe79omb5+XOYVj73J+Wjq/fz53DMJnzO5SNvkmm9QW4EPjvwGHgL4B/tNF9Wud4ekAB/wc42C1/DpzZje8Q8AjwZ8DrB7Y7B/gT4Ajwl8CvbPRYhhzvbvr/qP2vbmz3AOefam43+7wDDwJXD7yfiPkFbqX/JMM88BRwdD1z2fo8LzXe5c7hSZjnZca77Dk8ifM7UPu5c3gS5neti1+hL0mSmuVHP5IkqVkGFUmS1CyDiiRJapZBRZIkNcugIkmSmmVQkSRJzTKoSJKkZhlUJElSswwqkiSpWf8f7EEUC//OOlkAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"figure(figsize=(8, 4), dpi=80)\nplt.hist(df.tx_bytes, bins=20, color='r')\nplt.title('TX_BYTES - Transmitted Bytes')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:13.493595Z","iopub.execute_input":"2024-04-18T15:37:13.493852Z","iopub.status.idle":"2024-04-18T15:37:13.755749Z","shell.execute_reply.started":"2024-04-18T15:37:13.493810Z","shell.execute_reply":"2024-04-18T15:37:13.755036Z"},"trusted":true},"execution_count":21,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x320 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"figure(figsize=(8, 4), dpi=80)\nplt.hist(df.tx_kbps, bins=10, color='g')\nplt.title('TX_KBPC')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:13.756905Z","iopub.execute_input":"2024-04-18T15:37:13.757188Z","iopub.status.idle":"2024-04-18T15:37:13.923297Z","shell.execute_reply.started":"2024-04-18T15:37:13.757150Z","shell.execute_reply":"2024-04-18T15:37:13.922346Z"},"trusted":true},"execution_count":22,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x320 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.hist(df.switch, bins=20, color='r')\nplt.title('SWITCH')\nplt.xlabel('SWITCH')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:13.925336Z","iopub.execute_input":"2024-04-18T15:37:13.925715Z","iopub.status.idle":"2024-04-18T15:37:14.162118Z","shell.execute_reply.started":"2024-04-18T15:37:13.925664Z","shell.execute_reply":"2024-04-18T15:37:14.161342Z"},"trusted":true},"execution_count":23,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.hist(df[df['label'] == 1].switch, bins=20, color='r')\nplt.title('SWITCH')\nplt.xlabel('SWITCH')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:14.163171Z","iopub.execute_input":"2024-04-18T15:37:14.163377Z","iopub.status.idle":"2024-04-18T15:37:14.333923Z","shell.execute_reply.started":"2024-04-18T15:37:14.163352Z","shell.execute_reply":"2024-04-18T15:37:14.332902Z"},"trusted":true},"execution_count":24,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### II. Classical ML models","metadata":{}},{"cell_type":"code","source":"\nclass Model:\n    global y\n    def __init__(self, data):\n        self.data = data\n        X = preprocessing.StandardScaler().fit(self.data).transform(self.data)\n        self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(X, y, random_state=42, test_size=0.3)  \n    \n    def LogisticRegression(self):\n        solvers = ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']\n\n        start_time = time.time()\n        results_lr = []\n        accuracy_list = []\n        for solver in solvers:\n            LR = LogisticRegression(C=0.03, solver=solver).fit(self.X_train, self.y_train)\n            predicted_lr = LR.predict(self.X_test)\n            accuracy_lr = accuracy_score(self.y_test, predicted_lr)\n            #print(\"Accuracy: %.2f%%\" % (accuracy_lr * 100.0))\n            #print('################################################################')\n            results_lr.append({'solver' : solver, 'accuracy': str(round(accuracy_lr * 100, 2)) + \"%\", \n                                  'Coefficients': {'W' : LR.coef_, 'b': LR.intercept_}})\n            \n            accuracy_list.append(accuracy_lr)\n       \n        solver_name = solvers[accuracy_list.index(max(accuracy_list))]\n        LR = LogisticRegression(C=0.03, solver=solver_name).fit(self.X_train,self.y_train)\n        predicted_lr = LR.predict(self.X_test)\n        accuracy_lr = accuracy_score(self.y_test, predicted_lr)\n        print(\"Accuracy: %.2f%%\" % (accuracy_lr * 100.0), '\\n')\n        print(\"########################################################################\")\n        print('Best solver is : ', solver_name)\n        print(\"########################################################################\")\n        print(classification_report(predicted_lr, self.y_test), '\\n')\n        print(\"########################################################################\")\n        print(\"--- %s seconds --- time for LogisticRegression\" % (time.time() - start_time))\n        \n        \n    def SupportVectorMachine(self):\n        start_time = time.time()\n        accuracy_list = []\n        result_svm = []\n        kernels = ['linear', 'poly','rbf', 'sigmoid']\n        #kernels = ['rbf']\n        for kernel in kernels:\n            SVM = svm.SVC(kernel=kernel).fit(self.X_train, self.y_train)\n            predicted_svm = SVM.predict(self.X_test)\n            accuracy_svm = accuracy_score(self.y_test, predicted_svm)\n            result_svm.append({\"kernel\" : kernel, \"accuracy\": f\"{round(accuracy_svm*100,2)}%\"})\n            print(\"Accuracy: %.2f%%\" % round((accuracy_svm * 100.0),2))\n            print('######################################################################')\n            accuracy_list.append(accuracy_svm)\n        \n        kernel_name = kernels[accuracy_list.index(max(accuracy_list))]\n        SVM = svm.SVC(kernel=kernel_name).fit(self.X_train, self.y_train)\n        predicted_svm = SVM.predict(self.X_test)\n        accuracy_svm = accuracy_score(self.y_test, predicted_svm)\n        print(f\"Accuracy of SVM model {round(accuracy_svm,2)*100}%\", '\\n')\n        print(\"########################################################################\")\n        print('best kernel is : ', kernel_name)\n        print(\"########################################################################\")\n        print(classification_report(predicted_svm, self.y_test))\n        print(\"########################################################################\")\n        print(\"--- %s seconds ---\" % (time.time() - start_time))\n        \n    def KNearetsNeighbor(self):\n        start_time = time.time()\n        Ks = 12\n        accuracy_knn = np.zeros((Ks-1))\n        std_acc = np.zeros((Ks-1))\n        #print(accuracy_knn)\n        for n in range(1,Ks):\n\n            #Train Model and Predict  \n            neigh = KNeighborsClassifier(n_neighbors = n).fit(self.X_train,self.y_train)\n            yhat=neigh.predict(self.X_test)\n            accuracy_knn[n-1] = metrics.accuracy_score(self.y_test, yhat)\n\n\n            std_acc[n-1]=np.std(yhat==self.y_test)/np.sqrt(yhat.shape[0])\n\n        #print(accuracy_knn,'\\n\\n') # courseranyn ozinde tek osy gana jazylyp turdy\n        #print(std_acc)\n        #accuracy_knn[0] = 0\n        plt.figure(figsize=(10,6))\n        plt.plot(range(1,Ks),accuracy_knn,'g')\n        plt.fill_between(range(1,Ks),accuracy_knn - 1 * std_acc,accuracy_knn + 1 * std_acc, alpha=0.10)\n        plt.fill_between(range(1,Ks),accuracy_knn - 3 * std_acc,accuracy_knn + 3 * std_acc, alpha=0.10,color=\"green\")\n        plt.legend(('Accuracy ', '+/- 1xstd','+/- 3xstd'))\n        plt.ylabel('Accuracy ')\n        plt.xlabel('Number of Neighbors (K)')\n        plt.tight_layout()\n        plt.show()\n        \n        \n        knnc = KNeighborsClassifier()\n        knnc_search = GridSearchCV(knnc, param_grid={'n_neighbors': [3, 5, 10],\n                                             'weights': ['uniform', 'distance'],\n                                             'metric': ['euclidean', 'manhattan']},\n                           n_jobs=-1, cv=3, scoring='accuracy', verbose=2)\n        \n        knnc_search.fit(self.X_train, self.y_train)\n        #print(knnc_search.best_params_)\n        #print(knnc_search.best_score_)\n        n_neighbors = knnc_search.best_params_['n_neighbors']\n        weights = knnc_search.best_params_['weights']\n        metric = knnc_search.best_params_['metric']\n        KNN = KNeighborsClassifier(n_neighbors=n_neighbors, metric=metric, weights=weights).fit(self.X_train,self.y_train)\n        \n        predicted_knn = KNN.predict(self.X_test)\n        accuracy_knn = metrics.accuracy_score(self.y_test, predicted_knn)\n        print(f\"Accuracy of KNN model {round(accuracy_knn,2)*100}%\", '\\n')\n        print(\"########################################################################\")\n        print(classification_report(predicted_knn, self.y_test))\n        print(\"########################################################################\")\n        print(\"--- %s seconds ---\" % (time.time() - start_time))\n        \n    def DecisionTree(self):\n        start_time = time.time()\n        tree = DecisionTreeClassifier()\n        dt_search = GridSearchCV(tree, param_grid={'criterion' : ['gini', 'entropy'],\n                                           'max_depth' : [2,3,4,5,6,7,8, 9, 10],\n                                           'max_leaf_nodes' : [2,3,4,5,6,7,8,9,10, 11]},\n                           n_jobs=-1, cv=5, scoring='accuracy', verbose=2)\n        \n        dt_search.fit(self.X_train, self.y_train)\n        \n        criterion = dt_search.best_params_['criterion']\n        max_depth = dt_search.best_params_['max_depth']\n        max_leaf_nodes = dt_search.best_params_['max_leaf_nodes']\n        \n        dtree = DecisionTreeClassifier(criterion=criterion, \n                                       max_depth=max_depth, \n                                       max_leaf_nodes=max_leaf_nodes).fit(self.X_train, self.y_train)\n        predicted_dt = dtree.predict(self.X_test)\n        accuracy_dt = metrics.accuracy_score(self.y_test, predicted_dt)\n        print(f\"criterion: {criterion}, max depth: {max_depth}, max_leaf: {max_leaf_nodes}\")\n        print(f\"The Accuracy is : {round(accuracy_dt * 100,2)}%\")\n        print(\"########################################################################\")\n        print(classification_report(predicted_dt, self.y_test))\n        print(\"########################################################################\")\n        \n        print(\"--- %s seconds ---\" % (time.time() - start_time))\n    \n    def RandomForest(self):\n        start_time = time.time()\n        RF = RandomForestClassifier(criterion='gini', \n                                     n_estimators=500,\n                                     min_samples_split=10,\n                                     #min_samples_leaf=1,\n                                     max_features='auto',\n                                     oob_score=True,\n                                     random_state=1,\n                                     n_jobs=-1).fit(self.X_train, self.y_train)\n        \n        predicted_rf = RF.predict(self.X_test)\n        svm_accuracy = accuracy_score(self.y_test, predicted_rf)\n        print(f\"Accuracy of RF is : {round(svm_accuracy*100,2)}%\", '\\n')\n        print(\"########################################################################\")\n        print(classification_report(predicted_rf, self.y_test))\n        print(\"########################################################################\")\n        \n        print(\"--- %s seconds ---\" % (time.time() - start_time))\n\n\"\"\"\nDecision Tree works Well\nSuppert Vector Machine works well\nLogistic Regression works well\nKNN works well\nRandom Forest works well\n\"\"\"","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:14.336041Z","iopub.execute_input":"2024-04-18T15:37:14.336634Z","iopub.status.idle":"2024-04-18T15:37:14.394796Z","shell.execute_reply.started":"2024-04-18T15:37:14.336563Z","shell.execute_reply":"2024-04-18T15:37:14.393943Z"}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### III .Prediction Without Feature Selection","metadata":{}},{"cell_type":"markdown","source":"##### II-1. Data Preprocessing","metadata":{}},{"cell_type":"code","source":"df = data.copy()\ndf = df.dropna()","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:14.395897Z","iopub.execute_input":"2024-04-18T15:37:14.396141Z","iopub.status.idle":"2024-04-18T15:37:14.464158Z","shell.execute_reply.started":"2024-04-18T15:37:14.396105Z","shell.execute_reply":"2024-04-18T15:37:14.463344Z"},"trusted":true},"execution_count":26,"outputs":[]},{"cell_type":"code","source":"X = df.drop(['dt','src','dst','label'], axis=1)\ny = df.label","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:14.465256Z","iopub.execute_input":"2024-04-18T15:37:14.465485Z","iopub.status.idle":"2024-04-18T15:37:14.473846Z","shell.execute_reply.started":"2024-04-18T15:37:14.465457Z","shell.execute_reply":"2024-04-18T15:37:14.473057Z"},"trusted":true},"execution_count":27,"outputs":[]},{"cell_type":"code","source":"X = pd.get_dummies(X)","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:14.478114Z","iopub.execute_input":"2024-04-18T15:37:14.478389Z","iopub.status.idle":"2024-04-18T15:37:14.513386Z","shell.execute_reply.started":"2024-04-18T15:37:14.478354Z","shell.execute_reply":"2024-04-18T15:37:14.512470Z"},"trusted":true},"execution_count":28,"outputs":[]},{"cell_type":"code","source":"M = Model(X)","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:14.514528Z","iopub.execute_input":"2024-04-18T15:37:14.514721Z","iopub.status.idle":"2024-04-18T15:37:14.580650Z","shell.execute_reply.started":"2024-04-18T15:37:14.514698Z","shell.execute_reply":"2024-04-18T15:37:14.579772Z"},"trusted":true},"execution_count":29,"outputs":[]},{"cell_type":"code","source":"## Logistic Regression(Without FS)\nM.LogisticRegression()","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:14.582069Z","iopub.execute_input":"2024-04-18T15:37:14.582379Z","iopub.status.idle":"2024-04-18T15:37:19.755453Z","shell.execute_reply.started":"2024-04-18T15:37:14.582338Z","shell.execute_reply":"2024-04-18T15:37:19.754451Z"},"trusted":true},"execution_count":30,"outputs":[{"name":"stdout","text":"Accuracy: 76.64% \n\n########################################################################\nBest solver is :  liblinear\n########################################################################\n              precision    recall  f1-score   support\n\n           0       0.84      0.79      0.81     20024\n           1       0.66      0.72      0.69     11128\n\n    accuracy                           0.77     31152\n   macro avg       0.75      0.76      0.75     31152\nweighted avg       0.77      0.77      0.77     31152\n \n\n########################################################################\n--- 5.16776704788208 seconds --- time for LogisticRegression\n","output_type":"stream"}]},{"cell_type":"code","source":"## Support Vector Machine(Without FS)\nM.SupportVectorMachine()","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:37:19.757025Z","iopub.execute_input":"2024-04-18T15:37:19.757877Z","iopub.status.idle":"2024-04-18T15:50:51.445057Z","shell.execute_reply.started":"2024-04-18T15:37:19.757820Z","shell.execute_reply":"2024-04-18T15:50:51.444243Z"},"trusted":true},"execution_count":31,"outputs":[{"name":"stdout","text":"Accuracy: 78.40%\n######################################################################\nAccuracy: 96.53%\n######################################################################\nAccuracy: 96.67%\n######################################################################\nAccuracy: 54.52%\n######################################################################\nAccuracy of SVM model 97.0% \n\n########################################################################\nbest kernel is :  rbf\n########################################################################\n              precision    recall  f1-score   support\n\n           0       0.97      0.98      0.97     18750\n           1       0.97      0.95      0.96     12402\n\n    accuracy                           0.97     31152\n   macro avg       0.97      0.96      0.97     31152\nweighted avg       0.97      0.97      0.97     31152\n\n########################################################################\n--- 811.6821701526642 seconds ---\n","output_type":"stream"}]},{"cell_type":"code","source":"## Decision Tree(Without FS)\nM.DecisionTree()","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:50:51.446202Z","iopub.execute_input":"2024-04-18T15:50:51.446431Z","iopub.status.idle":"2024-04-18T15:52:07.538158Z","shell.execute_reply.started":"2024-04-18T15:50:51.446403Z","shell.execute_reply":"2024-04-18T15:52:07.537058Z"},"trusted":true},"execution_count":32,"outputs":[{"name":"stdout","text":"Fitting 5 folds for each of 180 candidates, totalling 900 fits\n","output_type":"stream"},{"name":"stderr","text":"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:    4.2s\n[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:   12.4s\n","output_type":"stream"},{"name":"stdout","text":"[CV] criterion=gini, max_depth=2, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=4, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=5, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=2, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=2, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=2, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=7, total=   0.3s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=8, total=   0.3s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=8, total=   0.3s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=3, max_leaf_nodes=10, total=   0.3s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=3, max_leaf_nodes=11, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=7, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=8, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=9, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=4, max_leaf_nodes=10, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=4, max_leaf_nodes=10, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=4, max_leaf_nodes=11, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=8, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=8, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=9, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=5, max_leaf_nodes=10, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=5, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=7, total=   0.4s[CV] criterion=gini, max_depth=2, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=4, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=5, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=5, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=2, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=2, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=7, total=   0.3s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=3, max_leaf_nodes=10, total=   0.3s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=3, max_leaf_nodes=10, total=   0.3s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=3, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=7, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=8, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=8, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=9, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=4, max_leaf_nodes=10, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=4, max_leaf_nodes=11, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=7, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=8, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=9, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=9, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=5, max_leaf_nodes=10, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=5, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=7, total=   0.4s[CV] criterion=gini, max_depth=2, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=4, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=5, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=2, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=2, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=2, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=7, total=   0.3s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=9, total=   0.3s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=3, max_leaf_nodes=10, total=   0.3s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=3, max_leaf_nodes=11, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=7, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=7, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=8, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=9, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=4, max_leaf_nodes=10, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=4, max_leaf_nodes=11, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=4, max_leaf_nodes=11, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=7, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=8, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=9, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=5, max_leaf_nodes=10, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=5, max_leaf_nodes=10, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=5, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=8, total=   0.4s[CV] criterion=gini, max_depth=2, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=4, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=4, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=5, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=2, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=2, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=4, total=   0.4s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=7, total=   0.3s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=7, total=   0.3s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=9, total=   0.3s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=3, max_leaf_nodes=10, total=   0.3s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=3, max_leaf_nodes=11, total=   0.3s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=3, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=7, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=8, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=9, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=9, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=4, max_leaf_nodes=10, total=   0.3s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=4, max_leaf_nodes=11, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=7, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=7, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=8, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=9, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=5, max_leaf_nodes=10, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=5, max_leaf_nodes=11, total=   0.3s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=5, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=8, total=   0.4s","output_type":"stream"},{"name":"stderr","text":"[Parallel(n_jobs=-1)]: Done 357 tasks      | elapsed:   29.4s\n","output_type":"stream"},{"name":"stdout","text":"\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=6, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=6, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=6, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=7, total=   0.3s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=8, total=   0.3s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=7, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=7, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=7, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=8, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=8, max_leaf_nodes=11, total=   0.5s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=9, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=9, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=9, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=2 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=3 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=4 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=5 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=5 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=6 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=7 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=8 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=9 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=9 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=10 .................\n[CV] .. criterion=gini, max_depth=10, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=11 .................\n[CV] .. criterion=gini, max_depth=10, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=6, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=6, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=7, total=   0.3s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=7, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=7, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=8, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=8, max_leaf_nodes=11, total=   0.5s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=2, total=   0.3s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=3, total=   0.5s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=5, total=   0.5s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=9, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=9, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=2 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=3 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=3 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=4 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=5 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=6 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=7 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=7, total=   0.3s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=7 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=8 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=9 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=10 .................\n[CV] .. criterion=gini, max_depth=10, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=11 .................\n[CV] .. criterion=gini, max_depth=10, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=11 .................\n[CV] .. criterion=gini, max_depth=10, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=4, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=6, max_leaf_nodes=10, total=   0.3s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=6, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=6, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=7, total=   0.3s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=7, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=7, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=8, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=8, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=8, max_leaf_nodes=11, total=   0.6s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=4, total=   0.4s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=4, total=   0.5s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=9, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=9, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=2 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=2 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=3 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=4 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=4 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=5 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=6 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=7 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=7, total=   0.3s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=8 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=8 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=9 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=10 .................\n[CV] .. criterion=gini, max_depth=10, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=11 .................\n[CV] .. criterion=gini, max_depth=10, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=4, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=9, total=   0.3s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=6, max_leaf_nodes=10, total=   0.3s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=6, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=7, max_leaf_nodes=10, total=   0.3s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=7, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=7, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=8, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=8, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=8, max_leaf_nodes=11, total=   0.5s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=2, total=   0.3s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=3, total=   0.4s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=4, total=   0.5s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=9, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=9, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=9, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=2 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=3 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=4 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=5 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=6 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=6 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=7 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=8 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=9 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=10 .................\n[CV] .. criterion=gini, max_depth=10, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=10 .................\n[CV] .. criterion=gini, max_depth=10, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=11 .................\n[CV] .. criterion=gini, max_depth=10, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=4, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=4, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=5, total=   0.2s","output_type":"stream"},{"name":"stderr","text":"[Parallel(n_jobs=-1)]: Done 640 tasks      | elapsed:   51.1s\n","output_type":"stream"},{"name":"stdout","text":"\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=4, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=5, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=2, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=2, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=2, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=7, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=8, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=9, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=9, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=3, max_leaf_nodes=10, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=3, max_leaf_nodes=11, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=7, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=8, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=9, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=4, max_leaf_nodes=10, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=4, max_leaf_nodes=11, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=4, max_leaf_nodes=11, total=   0.3s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=5, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=5, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=5, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=5, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=5, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=2, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=2, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=7, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=7, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=8, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=9, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=3, max_leaf_nodes=10, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=3, max_leaf_nodes=11, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=3, max_leaf_nodes=11, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=7, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=8, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=9, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=9, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=4, max_leaf_nodes=10, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=4, max_leaf_nodes=11, total=   0.3s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=5, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=5, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=6, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=5, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=2, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=2, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=2, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=7, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=8, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=8, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=9, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=3, max_leaf_nodes=10, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=3, max_leaf_nodes=11, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=7, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=8, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=9, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=4, max_leaf_nodes=10, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=4, max_leaf_nodes=10, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=4, max_leaf_nodes=11, total=   0.3s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=5, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=5, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=5, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=6, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=6, max_leaf_nodes=10, total=   0.5s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=2, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=2, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=7, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=8, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=9, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=3, max_leaf_nodes=10, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=3, max_leaf_nodes=10, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=3, max_leaf_nodes=11, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=6, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=7, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=8, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=8, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=9, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=4, max_leaf_nodes=10, total=   0.3s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=4, max_leaf_nodes=11, total=   0.3s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=5, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=5, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=6, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=6, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=6, max_leaf_nodes=11, total=   0.4s","output_type":"stream"},{"name":"stderr","text":"[Parallel(n_jobs=-1)]: Done 900 out of 900 | elapsed:  1.3min finished\n","output_type":"stream"},{"name":"stdout","text":"criterion: gini, max depth: 8, max_leaf: 11\nThe Accuracy is : 98.22%\n########################################################################\n              precision    recall  f1-score   support\n\n           0       0.98      0.99      0.99     18743\n           1       0.99      0.97      0.98     12409\n\n    accuracy                           0.98     31152\n   macro avg       0.98      0.98      0.98     31152\nweighted avg       0.98      0.98      0.98     31152\n\n########################################################################\n--- 76.08702301979065 seconds ---\n","output_type":"stream"}]},{"cell_type":"code","source":"## Random Forest Classification(Without FS)\nM.RandomForest()","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:52:07.539884Z","iopub.execute_input":"2024-04-18T15:52:07.540228Z","iopub.status.idle":"2024-04-18T15:52:25.754931Z","shell.execute_reply.started":"2024-04-18T15:52:07.540186Z","shell.execute_reply":"2024-04-18T15:52:25.754147Z"},"trusted":true},"execution_count":33,"outputs":[{"name":"stdout","text":"Accuracy of RF is : 99.99% \n\n########################################################################\n              precision    recall  f1-score   support\n\n           0       1.00      1.00      1.00     18984\n           1       1.00      1.00      1.00     12168\n\n    accuracy                           1.00     31152\n   macro avg       1.00      1.00      1.00     31152\nweighted avg       1.00      1.00      1.00     31152\n\n########################################################################\n--- 18.209091424942017 seconds ---\n","output_type":"stream"}]},{"cell_type":"code","source":"M.KNearetsNeighbor()","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:52:25.756027Z","iopub.execute_input":"2024-04-18T15:52:25.756246Z","iopub.status.idle":"2024-04-18T15:55:04.313688Z","shell.execute_reply.started":"2024-04-18T15:52:25.756219Z","shell.execute_reply":"2024-04-18T15:55:04.312849Z"},"trusted":true},"execution_count":34,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 720x432 with 1 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\n"},"metadata":{"needs_background":"light"}},{"name":"stdout","text":"Fitting 3 folds for each of 12 candidates, totalling 36 fits\n","output_type":"stream"},{"name":"stderr","text":"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n","output_type":"stream"},{"name":"stdout","text":"\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=6, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=7, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=7, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=2, total=   0.3s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=3, total=   0.5s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=4, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=8, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=8, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=8, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=9, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=9, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=2 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=3 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=3 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=4 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=5 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=6 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=7 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=7 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=8 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=9 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=10 ..............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=11 ..............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=11 ..............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=11, total=   0.4s\n[CV] metric=euclidean, n_neighbors=3, weights=distance ...............\n[CV]  metric=euclidean, n_neighbors=3, weights=distance, total=   5.6s\n[CV] metric=euclidean, n_neighbors=3, weights=distance ...............\n[CV]  metric=euclidean, n_neighbors=3, weights=distance, total=   5.3s\n[CV] metric=euclidean, n_neighbors=5, weights=uniform ................\n[CV] . metric=euclidean, n_neighbors=5, weights=uniform, total=   7.7s\n[CV] metric=euclidean, n_neighbors=10, weights=uniform ...............\n[CV]  metric=euclidean, n_neighbors=10, weights=uniform, total=  10.3s\n[CV] metric=euclidean, n_neighbors=10, weights=distance ..............\n[CV]  metric=euclidean, n_neighbors=10, weights=distance, total=   8.5s\n[CV] metric=manhattan, n_neighbors=3, weights=distance ...............\n[CV]  metric=manhattan, n_neighbors=3, weights=distance, total=   6.8s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=6, max_leaf_nodes=10, total=   0.5s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=6, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=7, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=7, max_leaf_nodes=11, total=   0.5s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=7, max_leaf_nodes=11, total=   0.7s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=8, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=8, max_leaf_nodes=11, total=   0.5s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=9, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=9, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=9, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=2 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=3 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=4 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=5 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=6 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=6 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=7 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=8 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=9 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=10 ..............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=10 ..............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=11 ..............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=11, total=   0.4s\n[CV] metric=euclidean, n_neighbors=3, weights=uniform ................\n[CV] . metric=euclidean, n_neighbors=3, weights=uniform, total=   6.6s\n[CV] metric=euclidean, n_neighbors=5, weights=uniform ................\n[CV] . metric=euclidean, n_neighbors=5, weights=uniform, total=   7.5s\n[CV] metric=euclidean, n_neighbors=5, weights=distance ...............\n[CV]  metric=euclidean, n_neighbors=5, weights=distance, total=   6.4s\n[CV] metric=euclidean, n_neighbors=10, weights=uniform ...............\n[CV]  metric=euclidean, n_neighbors=10, weights=uniform, total=   9.6s\n[CV] metric=manhattan, n_neighbors=3, weights=uniform ................\n[CV] . metric=manhattan, n_neighbors=3, weights=uniform, total=   7.7s\n[CV] metric=manhattan, n_neighbors=3, weights=distance ...............\n[CV]  metric=manhattan, n_neighbors=3, weights=distance, total=   6.6s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=7, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=7, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=7, max_leaf_nodes=11, total=   0.6s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=2, total=   0.3s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=4, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=8, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=8, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=5, total=   0.3s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=9, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=9, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=2 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=2 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=3 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=4 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=4 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=5 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=6 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=7 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=8 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=8 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=9 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=10 ..............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=11 ..............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=11, total=   0.4s\n[CV] metric=euclidean, n_neighbors=3, weights=uniform ................\n[CV] . metric=euclidean, n_neighbors=3, weights=uniform, total=   6.5s\n[CV] metric=euclidean, n_neighbors=3, weights=distance ...............\n[CV]  metric=euclidean, n_neighbors=3, weights=distance, total=   5.4s\n[CV] metric=euclidean, n_neighbors=5, weights=distance ...............\n[CV]  metric=euclidean, n_neighbors=5, weights=distance, total=   6.2s\n[CV] metric=euclidean, n_neighbors=10, weights=uniform ...............\n[CV]  metric=euclidean, n_neighbors=10, weights=uniform, total=  10.1s\n[CV] metric=euclidean, n_neighbors=10, weights=distance ..............\n[CV]  metric=euclidean, n_neighbors=10, weights=distance, total=   8.1s\n[CV] metric=manhattan, n_neighbors=3, weights=uniform ................\n[CV] . metric=manhattan, n_neighbors=3, weights=uniform, total=   7.8s\n[CV] metric=manhattan, n_neighbors=5, weights=uniform ................\n[CV] . metric=manhattan, n_neighbors=5, weights=uniform, total=  10.1s\n[CV] metric=manhattan, n_neighbors=5, weights=distance ...............\n[CV]  metric=manhattan, n_neighbors=5, weights=distance, total=   8.9s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=6, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=7, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=7, max_leaf_nodes=11, total=   0.7s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=2, total=   0.3s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=3, total=   0.5s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=4, total=   0.5s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=8, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=8, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=8, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=entropy, max_depth=8, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=8, max_leaf_nodes=11, total=   0.5s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=9, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=9, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=9, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=entropy, max_depth=9, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=9, max_leaf_nodes=11, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=2 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=2, total=   0.2s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=3 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=4 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=4, total=   0.3s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=5 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=5 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=5, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=6 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=6, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=7 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=7, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=8 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=8, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=9 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=9 ...............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=9, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=10 ..............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=10, total=   0.4s\n[CV] criterion=entropy, max_depth=10, max_leaf_nodes=11 ..............\n[CV]  criterion=entropy, max_depth=10, max_leaf_nodes=11, total=   0.4s\n[CV] metric=euclidean, n_neighbors=3, weights=uniform ................\n[CV] . metric=euclidean, n_neighbors=3, weights=uniform, total=   6.7s\n[CV] metric=euclidean, n_neighbors=5, weights=uniform ................\n[CV] . metric=euclidean, n_neighbors=5, weights=uniform, total=   7.7s\n[CV] metric=euclidean, n_neighbors=5, weights=distance ...............\n[CV]  metric=euclidean, n_neighbors=5, weights=distance, total=   7.3s\n[CV] metric=euclidean, n_neighbors=10, weights=distance ..............\n[CV]  metric=euclidean, n_neighbors=10, weights=distance, total=   8.8s\n[CV] metric=manhattan, n_neighbors=3, weights=uniform ................\n[CV] . metric=manhattan, n_neighbors=3, weights=uniform, total=   7.7s\n[CV] metric=manhattan, n_neighbors=3, weights=distance ...............\n[CV]  metric=manhattan, n_neighbors=3, weights=distance, total=   6.8s\n[CV] metric=manhattan, n_neighbors=5, weights=distance ...............\n[CV]  metric=manhattan, n_neighbors=5, weights=distance, total=   9.3s\n[CV] metric=manhattan, n_neighbors=5, weights=distance ...............\n[CV]  metric=manhattan, n_neighbors=5, weights=distance, total=   9.2s","output_type":"stream"},{"name":"stderr","text":"[Parallel(n_jobs=-1)]: Done  36 out of  36 | elapsed:  1.3min finished\n","output_type":"stream"},{"name":"stdout","text":"Accuracy of KNN model 98.0% \n\n########################################################################\n              precision    recall  f1-score   support\n\n           0       0.99      0.99      0.99     19002\n           1       0.98      0.98      0.98     12150\n\n    accuracy                           0.98     31152\n   macro avg       0.98      0.98      0.98     31152\nweighted avg       0.98      0.98      0.98     31152\n\n########################################################################\n--- 158.5532991886139 seconds ---\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### IV .Prediction With Feature Selection","metadata":{}},{"cell_type":"code","source":"df1 = data.copy()","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:55:04.314904Z","iopub.execute_input":"2024-04-18T15:55:04.315169Z","iopub.status.idle":"2024-04-18T15:55:04.324444Z","shell.execute_reply.started":"2024-04-18T15:55:04.315137Z","shell.execute_reply":"2024-04-18T15:55:04.323582Z"},"trusted":true},"execution_count":35,"outputs":[]},{"cell_type":"code","source":"df1 = df1.dropna()","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:55:04.325712Z","iopub.execute_input":"2024-04-18T15:55:04.325967Z","iopub.status.idle":"2024-04-18T15:55:04.384945Z","shell.execute_reply.started":"2024-04-18T15:55:04.325922Z","shell.execute_reply":"2024-04-18T15:55:04.384212Z"},"trusted":true},"execution_count":36,"outputs":[]},{"cell_type":"code","source":"df1.columns","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:55:04.386136Z","iopub.execute_input":"2024-04-18T15:55:04.386868Z","iopub.status.idle":"2024-04-18T15:55:04.392651Z","shell.execute_reply.started":"2024-04-18T15:55:04.386827Z","shell.execute_reply":"2024-04-18T15:55:04.391934Z"},"trusted":true},"execution_count":37,"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"Index(['dt', 'switch', 'src', 'dst', 'pktcount', 'bytecount', 'dur',\n       'dur_nsec', 'tot_dur', 'flows', 'packetins', 'pktperflow',\n       'byteperflow', 'pktrate', 'Pairflow', 'Protocol', 'port_no', 'tx_bytes',\n       'rx_bytes', 'tx_kbps', 'rx_kbps', 'tot_kbps', 'label'],\n      dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"df1.info()","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:55:04.393759Z","iopub.execute_input":"2024-04-18T15:55:04.393973Z","iopub.status.idle":"2024-04-18T15:55:04.447105Z","shell.execute_reply.started":"2024-04-18T15:55:04.393948Z","shell.execute_reply":"2024-04-18T15:55:04.446366Z"},"trusted":true},"execution_count":38,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nInt64Index: 103839 entries, 0 to 104344\nData columns (total 23 columns):\n #   Column       Non-Null Count   Dtype  \n---  ------       --------------   -----  \n 0   dt           103839 non-null  int64  \n 1   switch       103839 non-null  int64  \n 2   src          103839 non-null  object \n 3   dst          103839 non-null  object \n 4   pktcount     103839 non-null  int64  \n 5   bytecount    103839 non-null  int64  \n 6   dur          103839 non-null  int64  \n 7   dur_nsec     103839 non-null  int64  \n 8   tot_dur      103839 non-null  float64\n 9   flows        103839 non-null  int64  \n 10  packetins    103839 non-null  int64  \n 11  pktperflow   103839 non-null  int64  \n 12  byteperflow  103839 non-null  int64  \n 13  pktrate      103839 non-null  int64  \n 14  Pairflow     103839 non-null  int64  \n 15  Protocol     103839 non-null  object \n 16  port_no      103839 non-null  int64  \n 17  tx_bytes     103839 non-null  int64  \n 18  rx_bytes     103839 non-null  int64  \n 19  tx_kbps      103839 non-null  int64  \n 20  rx_kbps      103839 non-null  float64\n 21  tot_kbps     103839 non-null  float64\n 22  label        103839 non-null  int64  \ndtypes: float64(3), int64(17), object(3)\nmemory usage: 19.0+ MB\n","output_type":"stream"}]},{"cell_type":"markdown","source":"##### https://www.mdpi.com/2079-9292/10/11/1227/pdf\n##### So, I will use the Feature Selection result of this paper","metadata":{}},{"cell_type":"code","source":"important_features = [\n    'src',\n    'pktcount',\n    'dst',\n    'byteperflow',\n    'pktperflow',\n    'pktrate',\n    'tot_kbps',\n    'rx_kbps',\n    'flows',\n    'bytecount',\n    'dt',\n    'Protocol',\n    'dur',\n    'tot_dur'\n                      \n                     ]\n\n\nweights = [\n    17.87,\n    15.16,\n    13.64,\n    12.97,\n    11.35,\n    11.35,\n    9.68,\n    9.66,\n    8.95,\n    4.92,\n    2.33,\n    1.31,\n    1.11,\n    1.11\n]","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:55:04.448214Z","iopub.execute_input":"2024-04-18T15:55:04.448785Z","iopub.status.idle":"2024-04-18T15:55:04.454733Z","shell.execute_reply.started":"2024-04-18T15:55:04.448740Z","shell.execute_reply":"2024-04-18T15:55:04.453886Z"},"trusted":true},"execution_count":39,"outputs":[]},{"cell_type":"code","source":"weighted_features = pd.DataFrame({'features':important_features,\n                                 'weights':weights})\nweighted_features","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:55:04.455734Z","iopub.execute_input":"2024-04-18T15:55:04.455951Z","iopub.status.idle":"2024-04-18T15:55:04.471586Z","shell.execute_reply.started":"2024-04-18T15:55:04.455917Z","shell.execute_reply":"2024-04-18T15:55:04.470838Z"},"trusted":true},"execution_count":40,"outputs":[{"execution_count":40,"output_type":"execute_result","data":{"text/plain":"       features  weights\n0           src    17.87\n1      pktcount    15.16\n2           dst    13.64\n3   byteperflow    12.97\n4    pktperflow    11.35\n5       pktrate    11.35\n6      tot_kbps     9.68\n7       rx_kbps     9.66\n8         flows     8.95\n9     bytecount     4.92\n10           dt     2.33\n11     Protocol     1.31\n12          dur     1.11\n13      tot_dur     1.11","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>features</th>\n      <th>weights</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>src</td>\n      <td>17.87</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>pktcount</td>\n      <td>15.16</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>dst</td>\n      <td>13.64</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>byteperflow</td>\n      <td>12.97</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>pktperflow</td>\n      <td>11.35</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>pktrate</td>\n      <td>11.35</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>tot_kbps</td>\n      <td>9.68</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>rx_kbps</td>\n      <td>9.66</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>flows</td>\n      <td>8.95</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>bytecount</td>\n      <td>4.92</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>dt</td>\n      <td>2.33</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Protocol</td>\n      <td>1.31</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>dur</td>\n      <td>1.11</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>tot_dur</td>\n      <td>1.11</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"### But we dont need src, dst, dt, So, we will drop them\nX = df1[important_features]\ny = df1.label\n\nX = X.drop(['src', 'dst', 'dt'], axis=1)","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:55:04.472661Z","iopub.execute_input":"2024-04-18T15:55:04.472916Z","iopub.status.idle":"2024-04-18T15:55:04.491770Z","shell.execute_reply.started":"2024-04-18T15:55:04.472888Z","shell.execute_reply":"2024-04-18T15:55:04.491163Z"},"trusted":true},"execution_count":41,"outputs":[]},{"cell_type":"code","source":"X.head()","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:55:04.492976Z","iopub.execute_input":"2024-04-18T15:55:04.493616Z","iopub.status.idle":"2024-04-18T15:55:04.510887Z","shell.execute_reply.started":"2024-04-18T15:55:04.493568Z","shell.execute_reply":"2024-04-18T15:55:04.510042Z"},"trusted":true},"execution_count":42,"outputs":[{"execution_count":42,"output_type":"execute_result","data":{"text/plain":"   pktcount  byteperflow  pktperflow  pktrate  tot_kbps  rx_kbps  flows  \\\n0     45304     14428310       13535      451       0.0      0.0      3   \n1    126395     14424046       13531      451       0.0      0.0      2   \n2     90333     14427244       13534      451       0.0      0.0      3   \n3     90333     14427244       13534      451       0.0      0.0      3   \n4     90333     14427244       13534      451       0.0      0.0      3   \n\n   bytecount Protocol  dur       tot_dur  \n0   48294064      UDP  100  1.010000e+11  \n1  134737070      UDP  280  2.810000e+11  \n2   96294978      UDP  200  2.010000e+11  \n3   96294978      UDP  200  2.010000e+11  \n4   96294978      UDP  200  2.010000e+11  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>pktcount</th>\n      <th>byteperflow</th>\n      <th>pktperflow</th>\n      <th>pktrate</th>\n      <th>tot_kbps</th>\n      <th>rx_kbps</th>\n      <th>flows</th>\n      <th>bytecount</th>\n      <th>Protocol</th>\n      <th>dur</th>\n      <th>tot_dur</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>45304</td>\n      <td>14428310</td>\n      <td>13535</td>\n      <td>451</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3</td>\n      <td>48294064</td>\n      <td>UDP</td>\n      <td>100</td>\n      <td>1.010000e+11</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>126395</td>\n      <td>14424046</td>\n      <td>13531</td>\n      <td>451</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2</td>\n      <td>134737070</td>\n      <td>UDP</td>\n      <td>280</td>\n      <td>2.810000e+11</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>90333</td>\n      <td>14427244</td>\n      <td>13534</td>\n      <td>451</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3</td>\n      <td>96294978</td>\n      <td>UDP</td>\n      <td>200</td>\n      <td>2.010000e+11</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>90333</td>\n      <td>14427244</td>\n      <td>13534</td>\n      <td>451</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3</td>\n      <td>96294978</td>\n      <td>UDP</td>\n      <td>200</td>\n      <td>2.010000e+11</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>90333</td>\n      <td>14427244</td>\n      <td>13534</td>\n      <td>451</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3</td>\n      <td>96294978</td>\n      <td>UDP</td>\n      <td>200</td>\n      <td>2.010000e+11</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"abs(X.corr())","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:55:04.512150Z","iopub.execute_input":"2024-04-18T15:55:04.512438Z","iopub.status.idle":"2024-04-18T15:55:04.563523Z","shell.execute_reply.started":"2024-04-18T15:55:04.512399Z","shell.execute_reply":"2024-04-18T15:55:04.562808Z"},"trusted":true},"execution_count":43,"outputs":[{"execution_count":43,"output_type":"execute_result","data":{"text/plain":"             pktcount  byteperflow  pktperflow   pktrate  tot_kbps   rx_kbps  \\\npktcount     1.000000     0.290614    0.470065  0.470097  0.003054  0.002328   \nbyteperflow  0.290614     1.000000    0.812940  0.812940  0.150260  0.114963   \npktperflow   0.470065     0.812940    1.000000  0.999999  0.162416  0.124257   \npktrate      0.470097     0.812940    0.999999  1.000000  0.162461  0.124291   \ntot_kbps     0.003054     0.150260    0.162416  0.162461  1.000000  0.635545   \nrx_kbps      0.002328     0.114963    0.124257  0.124291  0.635545  1.000000   \nflows        0.257467     0.246311    0.212795  0.212924  0.240285  0.183821   \nbytecount    0.675792     0.533201    0.333530  0.333553  0.076920  0.058850   \ndur          0.023222     0.259843    0.329504  0.329764  0.218143  0.166916   \ntot_dur      0.023283     0.259831    0.329483  0.329744  0.218268  0.167011   \n\n                flows  bytecount       dur   tot_dur  \npktcount     0.257467   0.675792  0.023222  0.023283  \nbyteperflow  0.246311   0.533201  0.259843  0.259831  \npktperflow   0.212795   0.333530  0.329504  0.329483  \npktrate      0.212924   0.333553  0.329764  0.329744  \ntot_kbps     0.240285   0.076920  0.218143  0.218268  \nrx_kbps      0.183821   0.058850  0.166916  0.167011  \nflows        1.000000   0.244514  0.176351  0.176352  \nbytecount    0.244514   1.000000  0.038033  0.038034  \ndur          0.176351   0.038033  1.000000  0.999998  \ntot_dur      0.176352   0.038034  0.999998  1.000000  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>pktcount</th>\n      <th>byteperflow</th>\n      <th>pktperflow</th>\n      <th>pktrate</th>\n      <th>tot_kbps</th>\n      <th>rx_kbps</th>\n      <th>flows</th>\n      <th>bytecount</th>\n      <th>dur</th>\n      <th>tot_dur</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>pktcount</th>\n      <td>1.000000</td>\n      <td>0.290614</td>\n      <td>0.470065</td>\n      <td>0.470097</td>\n      <td>0.003054</td>\n      <td>0.002328</td>\n      <td>0.257467</td>\n      <td>0.675792</td>\n      <td>0.023222</td>\n      <td>0.023283</td>\n    </tr>\n    <tr>\n      <th>byteperflow</th>\n      <td>0.290614</td>\n      <td>1.000000</td>\n      <td>0.812940</td>\n      <td>0.812940</td>\n      <td>0.150260</td>\n      <td>0.114963</td>\n      <td>0.246311</td>\n      <td>0.533201</td>\n      <td>0.259843</td>\n      <td>0.259831</td>\n    </tr>\n    <tr>\n      <th>pktperflow</th>\n      <td>0.470065</td>\n      <td>0.812940</td>\n      <td>1.000000</td>\n      <td>0.999999</td>\n      <td>0.162416</td>\n      <td>0.124257</td>\n      <td>0.212795</td>\n      <td>0.333530</td>\n      <td>0.329504</td>\n      <td>0.329483</td>\n    </tr>\n    <tr>\n      <th>pktrate</th>\n      <td>0.470097</td>\n      <td>0.812940</td>\n      <td>0.999999</td>\n      <td>1.000000</td>\n      <td>0.162461</td>\n      <td>0.124291</td>\n      <td>0.212924</td>\n      <td>0.333553</td>\n      <td>0.329764</td>\n      <td>0.329744</td>\n    </tr>\n    <tr>\n      <th>tot_kbps</th>\n      <td>0.003054</td>\n      <td>0.150260</td>\n      <td>0.162416</td>\n      <td>0.162461</td>\n      <td>1.000000</td>\n      <td>0.635545</td>\n      <td>0.240285</td>\n      <td>0.076920</td>\n      <td>0.218143</td>\n      <td>0.218268</td>\n    </tr>\n    <tr>\n      <th>rx_kbps</th>\n      <td>0.002328</td>\n      <td>0.114963</td>\n      <td>0.124257</td>\n      <td>0.124291</td>\n      <td>0.635545</td>\n      <td>1.000000</td>\n      <td>0.183821</td>\n      <td>0.058850</td>\n      <td>0.166916</td>\n      <td>0.167011</td>\n    </tr>\n    <tr>\n      <th>flows</th>\n      <td>0.257467</td>\n      <td>0.246311</td>\n      <td>0.212795</td>\n      <td>0.212924</td>\n      <td>0.240285</td>\n      <td>0.183821</td>\n      <td>1.000000</td>\n      <td>0.244514</td>\n      <td>0.176351</td>\n      <td>0.176352</td>\n    </tr>\n    <tr>\n      <th>bytecount</th>\n      <td>0.675792</td>\n      <td>0.533201</td>\n      <td>0.333530</td>\n      <td>0.333553</td>\n      <td>0.076920</td>\n      <td>0.058850</td>\n      <td>0.244514</td>\n      <td>1.000000</td>\n      <td>0.038033</td>\n      <td>0.038034</td>\n    </tr>\n    <tr>\n      <th>dur</th>\n      <td>0.023222</td>\n      <td>0.259843</td>\n      <td>0.329504</td>\n      <td>0.329764</td>\n      <td>0.218143</td>\n      <td>0.166916</td>\n      <td>0.176351</td>\n      <td>0.038033</td>\n      <td>1.000000</td>\n      <td>0.999998</td>\n    </tr>\n    <tr>\n      <th>tot_dur</th>\n      <td>0.023283</td>\n      <td>0.259831</td>\n      <td>0.329483</td>\n      <td>0.329744</td>\n      <td>0.218268</td>\n      <td>0.167011</td>\n      <td>0.176352</td>\n      <td>0.038034</td>\n      <td>0.999998</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"fig, ax = plt.subplots(figsize=(10,7)) \nsns.heatmap(abs(X.corr()), annot=True)","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:55:04.564831Z","iopub.execute_input":"2024-04-18T15:55:04.565073Z","iopub.status.idle":"2024-04-18T15:55:05.448911Z","shell.execute_reply.started":"2024-04-18T15:55:04.565044Z","shell.execute_reply":"2024-04-18T15:55:05.448108Z"},"trusted":true},"execution_count":44,"outputs":[{"execution_count":44,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 720x504 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"### There some duplicated features and high correlated features","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:55:05.450144Z","iopub.execute_input":"2024-04-18T15:55:05.450398Z","iopub.status.idle":"2024-04-18T15:55:05.454434Z","shell.execute_reply.started":"2024-04-18T15:55:05.450366Z","shell.execute_reply":"2024-04-18T15:55:05.453495Z"},"trusted":true},"execution_count":45,"outputs":[]},{"cell_type":"code","source":"## \"dur\" and \"tot_dur\"\n## \"pktperflow\" and \"pktrate\"\n\nX = X.drop(['dur', \"pktrate\", \"pktperflow\"], axis=1)","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:55:05.456018Z","iopub.execute_input":"2024-04-18T15:55:05.456461Z","iopub.status.idle":"2024-04-18T15:55:05.468082Z","shell.execute_reply.started":"2024-04-18T15:55:05.456422Z","shell.execute_reply":"2024-04-18T15:55:05.467372Z"},"trusted":true},"execution_count":46,"outputs":[]},{"cell_type":"code","source":"X.columns","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:55:05.468977Z","iopub.execute_input":"2024-04-18T15:55:05.469206Z","iopub.status.idle":"2024-04-18T15:55:05.480144Z","shell.execute_reply.started":"2024-04-18T15:55:05.469168Z","shell.execute_reply":"2024-04-18T15:55:05.479343Z"},"trusted":true},"execution_count":47,"outputs":[{"execution_count":47,"output_type":"execute_result","data":{"text/plain":"Index(['pktcount', 'byteperflow', 'tot_kbps', 'rx_kbps', 'flows', 'bytecount',\n       'Protocol', 'tot_dur'],\n      dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"fig, ax = plt.subplots(figsize=(10,7)) \nsns.heatmap(abs(X.corr()), annot=True)","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:55:05.481216Z","iopub.execute_input":"2024-04-18T15:55:05.481474Z","iopub.status.idle":"2024-04-18T15:55:06.058300Z","shell.execute_reply.started":"2024-04-18T15:55:05.481439Z","shell.execute_reply":"2024-04-18T15:55:06.057569Z"},"trusted":true},"execution_count":48,"outputs":[{"execution_count":48,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 720x504 with 2 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"X = pd.get_dummies(X)","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:55:06.059385Z","iopub.execute_input":"2024-04-18T15:55:06.059590Z","iopub.status.idle":"2024-04-18T15:55:06.083247Z","shell.execute_reply.started":"2024-04-18T15:55:06.059563Z","shell.execute_reply":"2024-04-18T15:55:06.082519Z"},"trusted":true},"execution_count":49,"outputs":[]},{"cell_type":"code","source":"M = Model(X)","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:55:06.084483Z","iopub.execute_input":"2024-04-18T15:55:06.085012Z","iopub.status.idle":"2024-04-18T15:55:06.125513Z","shell.execute_reply.started":"2024-04-18T15:55:06.084958Z","shell.execute_reply":"2024-04-18T15:55:06.124891Z"},"trusted":true},"execution_count":50,"outputs":[]},{"cell_type":"code","source":"## Logistic Regression(With FS)\nM.LogisticRegression()","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:55:06.126720Z","iopub.execute_input":"2024-04-18T15:55:06.127035Z","iopub.status.idle":"2024-04-18T15:55:09.852140Z","shell.execute_reply.started":"2024-04-18T15:55:06.126983Z","shell.execute_reply":"2024-04-18T15:55:09.851223Z"},"trusted":true},"execution_count":51,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/scipy/optimize/linesearch.py:327: LineSearchWarning: The line search algorithm did not converge\n  warn('The line search algorithm did not converge', LineSearchWarning)\n/opt/conda/lib/python3.7/site-packages/sklearn/utils/optimize.py:204: UserWarning: Line Search failed\n  warnings.warn('Line Search failed')\n","output_type":"stream"},{"name":"stdout","text":"Accuracy: 75.21% \n\n########################################################################\nBest solver is :  newton-cg\n########################################################################\n              precision    recall  f1-score   support\n\n           0       0.85      0.77      0.81     20968\n           1       0.60      0.72      0.65     10184\n\n    accuracy                           0.75     31152\n   macro avg       0.73      0.74      0.73     31152\nweighted avg       0.77      0.75      0.76     31152\n \n\n########################################################################\n--- 3.719940185546875 seconds --- time for LogisticRegression\n","output_type":"stream"},{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/scipy/optimize/linesearch.py:327: LineSearchWarning: The line search algorithm did not converge\n  warn('The line search algorithm did not converge', LineSearchWarning)\n/opt/conda/lib/python3.7/site-packages/sklearn/utils/optimize.py:204: UserWarning: Line Search failed\n  warnings.warn('Line Search failed')\n","output_type":"stream"}]},{"cell_type":"code","source":"## Support Vector Machine\nM.SupportVectorMachine()","metadata":{"execution":{"iopub.status.busy":"2024-04-18T15:55:09.853864Z","iopub.execute_input":"2024-04-18T15:55:09.862187Z","iopub.status.idle":"2024-04-18T16:05:23.977642Z","shell.execute_reply.started":"2024-04-18T15:55:09.862117Z","shell.execute_reply":"2024-04-18T16:05:23.976832Z"},"trusted":true},"execution_count":52,"outputs":[{"name":"stdout","text":"Accuracy: 75.65%\n######################################################################\nAccuracy: 91.69%\n######################################################################\nAccuracy: 91.77%\n######################################################################\n\n[CV] metric=manhattan, n_neighbors=10, weights=distance ..............\n[CV]  metric=manhattan, n_neighbors=10, weights=distance, total=  12.0s\n\n[CV] metric=manhattan, n_neighbors=10, weights=uniform ...............\n[CV]  metric=manhattan, n_neighbors=10, weights=uniform, total=  13.0s\n\n[CV] metric=manhattan, n_neighbors=5, weights=uniform ................\n[CV] . metric=manhattan, n_neighbors=5, weights=uniform, total=  10.1s\n[CV] metric=manhattan, n_neighbors=10, weights=uniform ...............\n[CV]  metric=manhattan, n_neighbors=10, weights=uniform, total=  13.2s\n[CV] metric=manhattan, n_neighbors=10, weights=distance ..............\n[CV]  metric=manhattan, n_neighbors=10, weights=distance, total=  10.7s\n\n[CV] metric=manhattan, n_neighbors=5, weights=uniform ................\n[CV] . metric=manhattan, n_neighbors=5, weights=uniform, total=  10.4s\n[CV] metric=manhattan, n_neighbors=10, weights=uniform ...............\n[CV]  metric=manhattan, n_neighbors=10, weights=uniform, total=  12.8s\n[CV] metric=manhattan, n_neighbors=10, weights=distance ..............\n[CV]  metric=manhattan, n_neighbors=10, weights=distance, total=  10.7s\nAccuracy: 56.07%\n######################################################################\nAccuracy of SVM model 92.0% \n\n########################################################################\nbest kernel is :  rbf\n########################################################################\n              precision    recall  f1-score   support\n\n           0       0.90      0.96      0.93     17757\n           1       0.95      0.86      0.90     13395\n\n    accuracy                           0.92     31152\n   macro avg       0.92      0.91      0.91     31152\nweighted avg       0.92      0.92      0.92     31152\n\n########################################################################\n--- 614.1081471443176 seconds ---\n","output_type":"stream"}]},{"cell_type":"code","source":"M.RandomForest()","metadata":{"execution":{"iopub.status.busy":"2024-04-18T16:05:23.979134Z","iopub.execute_input":"2024-04-18T16:05:23.979807Z","iopub.status.idle":"2024-04-18T16:05:39.947620Z","shell.execute_reply.started":"2024-04-18T16:05:23.979754Z","shell.execute_reply":"2024-04-18T16:05:39.946770Z"},"trusted":true},"execution_count":53,"outputs":[{"name":"stdout","text":"Accuracy of RF is : 99.42% \n\n########################################################################\n              precision    recall  f1-score   support\n\n           0       0.99      1.00      1.00     18922\n           1       1.00      0.99      0.99     12230\n\n    accuracy                           0.99     31152\n   macro avg       0.99      0.99      0.99     31152\nweighted avg       0.99      0.99      0.99     31152\n\n########################################################################\n--- 15.962674379348755 seconds ---\n","output_type":"stream"}]},{"cell_type":"code","source":"M.DecisionTree()","metadata":{"execution":{"iopub.status.busy":"2024-04-18T16:05:39.948774Z","iopub.execute_input":"2024-04-18T16:05:39.949010Z","iopub.status.idle":"2024-04-18T16:06:16.912013Z","shell.execute_reply.started":"2024-04-18T16:05:39.948964Z","shell.execute_reply":"2024-04-18T16:06:16.911178Z"},"trusted":true},"execution_count":54,"outputs":[{"name":"stdout","text":"Fitting 5 folds for each of 180 candidates, totalling 900 fits\n","output_type":"stream"},{"name":"stderr","text":"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:    3.1s\n[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:    7.1s\n","output_type":"stream"},{"name":"stdout","text":"[CV] criterion=gini, max_depth=2, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=9, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=2, max_leaf_nodes=10, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=2, max_leaf_nodes=10, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=2, max_leaf_nodes=11, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=9, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=3, max_leaf_nodes=10, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=3, max_leaf_nodes=11, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=9, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=4, max_leaf_nodes=10, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=4, max_leaf_nodes=10, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=4, max_leaf_nodes=11, total=   0.1s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=4, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=4, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=5, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=5, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=5, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=5, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=7, total=   0.2s[CV] criterion=gini, max_depth=2, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=9, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=2, max_leaf_nodes=10, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=2, max_leaf_nodes=11, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=9, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=3, max_leaf_nodes=10, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=3, max_leaf_nodes=10, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=3, max_leaf_nodes=11, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=9, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=4, max_leaf_nodes=10, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=4, max_leaf_nodes=11, total=   0.1s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=4, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=5, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=5, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=5, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=7, total=   0.1s[CV] criterion=gini, max_depth=2, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=9, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=2, max_leaf_nodes=10, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=2, max_leaf_nodes=11, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=2, max_leaf_nodes=11, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=9, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=9, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=3, max_leaf_nodes=10, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=3, max_leaf_nodes=11, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=9, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=4, max_leaf_nodes=10, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=4, max_leaf_nodes=11, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=4, max_leaf_nodes=11, total=   0.1s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=4, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=5, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=9, total=   0.1s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=5, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=5, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=5, max_leaf_nodes=11, total=   0.1s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=7, total=   0.1s[CV] criterion=gini, max_depth=2, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=9, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=2, max_leaf_nodes=9, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=2, max_leaf_nodes=10, total=   0.1s\n[CV] criterion=gini, max_depth=2, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=2, max_leaf_nodes=11, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=3, max_leaf_nodes=9, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=3, max_leaf_nodes=10, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=3, max_leaf_nodes=11, total=   0.1s\n[CV] criterion=gini, max_depth=3, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=3, max_leaf_nodes=11, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=9, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=4, max_leaf_nodes=9, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=4, max_leaf_nodes=10, total=   0.1s\n[CV] criterion=gini, max_depth=4, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=4, max_leaf_nodes=11, total=   0.1s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=4, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=5, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=5, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=5, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=5, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=5, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=5, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=8, total=   0.1s","output_type":"stream"},{"name":"stderr","text":"[Parallel(n_jobs=-1)]: Done 357 tasks      | elapsed:   15.1s\n","output_type":"stream"},{"name":"stdout","text":"\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=6, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=6, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=6, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=5, total=   0.2s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=7, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=7, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=8, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=8, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=8, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=9, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=9, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=2 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=2 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=3 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=4 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=5 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=6 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=6 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=7 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=8 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=9 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=10 .................\n[CV] .. criterion=gini, max_depth=10, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=10 .................\n[CV] .. criterion=gini, max_depth=10, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=11 .................\n[CV] .. criterion=gini, max_depth=10, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=6, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=6, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=7, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=7, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=7, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=8, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=8, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=9, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=9, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=9, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=3 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=3 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=4 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=5 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=5, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=6 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=7 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=7 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=8 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=9 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=10 .................\n[CV] .. criterion=gini, max_depth=10, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=11 .................\n[CV] .. criterion=gini, max_depth=10, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=11 .................\n[CV] .. criterion=gini, max_depth=10, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=6, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=6, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=6, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=7, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=7, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=7, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=8, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=8, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=9, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=9, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=9, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=2 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=3 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=4 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=4 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=5 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=5, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=6 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=7 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=8 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=8 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=9 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=10 .................\n[CV] .. criterion=gini, max_depth=10, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=11 .................\n[CV] .. criterion=gini, max_depth=10, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=6, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=6, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=6, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=6, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=5, total=   0.2s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=7, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=7, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=7, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=7, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=5, total=   0.2s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=8, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=8, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=8, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=8, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=8, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=2 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=3 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=4 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=5, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=5 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=6 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=7 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=8 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=9 ...................\n[CV] .... criterion=gini, max_depth=9, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=10 ..................\n[CV] ... criterion=gini, max_depth=9, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=9, max_leaf_nodes=11 ..................\n[CV] ... criterion=gini, max_depth=9, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=2 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=2 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=3 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=4 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=5 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=5, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=5 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=5, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=6 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=7 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=8 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=9 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=9 ..................\n[CV] ... criterion=gini, max_depth=10, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=10 .................\n[CV] .. criterion=gini, max_depth=10, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=gini, max_depth=10, max_leaf_nodes=11 .................\n[CV] .. criterion=gini, max_depth=10, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=4, total=   0.1s","output_type":"stream"},{"name":"stderr","text":"[Parallel(n_jobs=-1)]: Done 640 tasks      | elapsed:   25.8s\n","output_type":"stream"},{"name":"stdout","text":"\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=9, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=2, max_leaf_nodes=10, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=2, max_leaf_nodes=11, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=9, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=9, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=3, max_leaf_nodes=10, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=3, max_leaf_nodes=11, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=9, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=4, max_leaf_nodes=10, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=4, max_leaf_nodes=11, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=4, max_leaf_nodes=11, total=   0.1s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=5, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=5, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=9, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=2, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=2, max_leaf_nodes=11, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=2, max_leaf_nodes=11, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=4, total=   0.2s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=9, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=3, max_leaf_nodes=10, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=3, max_leaf_nodes=11, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=4, total=   0.2s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=5, total=   0.2s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=4, max_leaf_nodes=10, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=4, max_leaf_nodes=10, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=4, max_leaf_nodes=11, total=   0.1s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=5, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=5, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=6, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=9, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=2, max_leaf_nodes=10, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=2, max_leaf_nodes=10, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=2, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=3, total=   0.2s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=9, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=3, max_leaf_nodes=10, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=3, max_leaf_nodes=11, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=3, max_leaf_nodes=11, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=4, total=   0.2s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=5, total=   0.2s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=9, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=9, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=4, max_leaf_nodes=10, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=4, max_leaf_nodes=11, total=   0.1s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=5, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=5, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=5, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=5, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=4, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=6, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=6, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=5, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=9, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=2, max_leaf_nodes=9, total=   0.1s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=2, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=entropy, max_depth=2, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=2, max_leaf_nodes=11, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=3, total=   0.3s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=4, total=   0.2s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=3, max_leaf_nodes=9, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=3, max_leaf_nodes=10, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=3, max_leaf_nodes=10, total=   0.1s\n[CV] criterion=entropy, max_depth=3, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=3, max_leaf_nodes=11, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=4, total=   0.2s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=6, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=7, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=8, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=4, max_leaf_nodes=9, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=4, max_leaf_nodes=10, total=   0.1s\n[CV] criterion=entropy, max_depth=4, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=4, max_leaf_nodes=11, total=   0.1s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=5, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=5, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=5, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=entropy, max_depth=5, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=5, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=2, total=   0.1s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=3 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=3, total=   0.1s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=4 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=4, total=   0.1s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=5 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=5, total=   0.1s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=6 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=6, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=7 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=7, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=8 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=8, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=9 ................\n[CV] . criterion=entropy, max_depth=6, max_leaf_nodes=9, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=10 ...............\n[CV]  criterion=entropy, max_depth=6, max_leaf_nodes=10, total=   0.2s\n[CV] criterion=entropy, max_depth=6, max_leaf_nodes=11 ...............\n[CV]  criterion=entropy, max_depth=6, max_leaf_nodes=11, total=   0.2s\n[CV] criterion=entropy, max_depth=7, max_leaf_nodes=2 ................\n[CV] . criterion=entropy, max_depth=7, max_leaf_nodes=2, total=   0.1s","output_type":"stream"},{"name":"stderr","text":"[Parallel(n_jobs=-1)]: Done 900 out of 900 | elapsed:   36.5s finished\n","output_type":"stream"},{"name":"stdout","text":"criterion: gini, max depth: 6, max_leaf: 11\nThe Accuracy is : 94.19%\n########################################################################\n              precision    recall  f1-score   support\n\n           0       0.91      1.00      0.95     17287\n           1       1.00      0.87      0.93     13865\n\n    accuracy                           0.94     31152\n   macro avg       0.95      0.94      0.94     31152\nweighted avg       0.95      0.94      0.94     31152\n\n########################################################################\n--- 36.958786725997925 seconds ---\n","output_type":"stream"}]}]}