breast_cancer_predict.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['id', 'diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',\n",
      "       'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
      "       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
      "       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n",
      "       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n",
      "       'fractal_dimension_se', 'radius_worst', 'texture_worst',\n",
      "       'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
      "       'compactness_worst', 'concavity_worst', 'concave points_worst',\n",
      "       'symmetry_worst', 'fractal_dimension_worst'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "cancer = pd.read_csv('breast_cancer.csv')\n",
    "print(cancer.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The dataset consists of 569 data points, with 32 features each:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dimension of cancer data: (569, 32)\n"
     ]
    }
   ],
   "source": [
    "print(\"dimension of cancer data: {}\".format(cancer.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "            ...             radius_worst  texture_worst  perimeter_worst  \\\n",
       "0           ...                    25.38          17.33           184.60   \n",
       "1           ...                    24.99          23.41           158.80   \n",
       "2           ...                    23.57          25.53           152.50   \n",
       "3           ...                    14.91          26.50            98.87   \n",
       "4           ...                    22.54          16.67           152.20   \n",
       "\n",
       "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Of these 569 data points, 212 are labeled as malignant and 357 as benign:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "diagnosis\n",
      "B    357\n",
      "M    212\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(cancer.groupby('diagnosis').size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a1bb069fd0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SugwISVKXASFJ6jIgJEldBoQkqcuAkCR1GRCSpC4DQpLUZUBIkroMCElSlwEhSeoyICRJ\nXQaEJKnLgJAkdRkQkqSuJRcQSc5N8kCSPUkunXY9krRcLamASLIC+H3gjcDpwEVJTp9uVZK0PC2p\ngADOBPZU1Ter6v8AnwTOn3JNkrQsLbWAWAM8NLa+t7VJkhbZymkXME86bfVjHZItwJa2+mSSBwav\navlYBXxn2kUsBbli87RL0I/zd3PO+3p/Jo/aT03SaakFxF5g3dj6WmDfeIeq2gpsXcyiloskO6pq\ndtp1SPP5uzkdS22K6WvAhiQvSfJ8YBOwbco1SdKytKRGEFV1MMm7gP8OrACurap7p1yWJC1LSyog\nAKpqO7B92nUsU07daanyd3MKUlUL95IkLTtL7RyEJGmJMCCWuSSV5GNj6yuTHEjyuWnWJQEkOZRk\nZ5K/THJ3kp+bdk3LyZI7B6FF9wPgFUlOrKq/Af4l8O0p1yTN+Zuq2giQ5A3AfwFeN92Slg9HEAL4\nE+AX2/JFwCemWIt0OD8BPDrtIpYTA0IweubVpiQnAD8L3DnleqQ5J7Yppq8Dfwj81rQLWk6cYhJV\ntSvJekajBy8x1lIyPsX0z4Abk7yivPxyUTiC0JxtwBU4vaQlqqr+gtEzmWamXcty4QhCc64FHquq\ne5KcPe1ipPmSvIzRExa+O+1algsDQgBU1V7gI9OuQ5rnxCQ723KAzVV1aJoFLSfeSS1J6vIchCSp\ny4CQJHUZEJKkLgNCktRlQEiSurzMVWqSvB94ktEzf+6oqi9MsZYPTLsGyYCQ5qmq91qD5BSTlrkk\n/yHJA0m+APyT1nZ9kgvb8nuTfC3J7iRbk6S1vybJriR/keS3k+xu7W9P8pkkn0/yjST/deyzLkpy\nTzvWh1rbivZ5u9u2X+3U8MEk97XPu2JR/wNpWXMEoWUryauBTcAZjP5fuBu4a16336uqD7T+HwP+\nNfDHwHXAlqr6cpIPzttnYzvm08ADST4KHAI+BLya0SOr/zTJBcBDwJqqekX7jJPn1Xgq8CbgZVVV\n87dLQ3IEoeXsnwOfraqnqupxRg8snO/nk9yZ5B7gHODl7Y/0SVX15dbnj+btc1tVPVZVPwTuA34K\neA1we1UdqKqDwMeBfwF8E/iHST6a5Fzg8XnHehz4IfCHSd4MPPV3/qmlCRkQWu4O+6yZ9v0YfwBc\nWFU/A1wNnMDomUBH8vTY8iFGo5PuPlX1KPBK4HbgEkbfeTC+/SBwJnAzcAHw+QU+W3rGGBBazu4A\n3pTkxCQnAb80b/sJ7f07SV4AXAj/74/6E0nOats3TfBZdwKvS7IqyQpG373x50lWAc+rqpuB/wi8\nanyn9rk/WVXbgfcwmr6SFoXnILRsVdXdST4F7AT+Gvgf87Z/P8nVwD3Ag8DXxjZfDFyd5AeM/vX/\n2AKftT/JZcAXGY0mtlfVLUleCVyXZO4fa5fN2/Uk4JY2mgnwq0f9g0rHyKe5SscgyQuq6sm2fCmw\nuqrePeWypGeUIwjp2PxiGxGsZDT6ePt0y5GeeY4gJEldnqSWJHUZEJKkLgNCktRlQEiSugwISVKX\nASFJ6vq/1RPLlnGm06wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a1baf56048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.countplot(cancer['diagnosis'],label=\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 32 columns):\n",
      "id                         569 non-null int64\n",
      "diagnosis                  569 non-null object\n",
      "radius_mean                569 non-null float64\n",
      "texture_mean               569 non-null float64\n",
      "perimeter_mean             569 non-null float64\n",
      "area_mean                  569 non-null float64\n",
      "smoothness_mean            569 non-null float64\n",
      "compactness_mean           569 non-null float64\n",
      "concavity_mean             569 non-null float64\n",
      "concave points_mean        569 non-null float64\n",
      "symmetry_mean              569 non-null float64\n",
      "fractal_dimension_mean     569 non-null float64\n",
      "radius_se                  569 non-null float64\n",
      "texture_se                 569 non-null float64\n",
      "perimeter_se               569 non-null float64\n",
      "area_se                    569 non-null float64\n",
      "smoothness_se              569 non-null float64\n",
      "compactness_se             569 non-null float64\n",
      "concavity_se               569 non-null float64\n",
      "concave points_se          569 non-null float64\n",
      "symmetry_se                569 non-null float64\n",
      "fractal_dimension_se       569 non-null float64\n",
      "radius_worst               569 non-null float64\n",
      "texture_worst              569 non-null float64\n",
      "perimeter_worst            569 non-null float64\n",
      "area_worst                 569 non-null float64\n",
      "smoothness_worst           569 non-null float64\n",
      "compactness_worst          569 non-null float64\n",
      "concavity_worst            569 non-null float64\n",
      "concave points_worst       569 non-null float64\n",
      "symmetry_worst             569 non-null float64\n",
      "fractal_dimension_worst    569 non-null float64\n",
      "dtypes: float64(30), int64(1), object(1)\n",
      "memory usage: 142.3+ KB\n"
     ]
    }
   ],
   "source": [
    "cancer.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We do not need \"id\" column for our analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cancer.drop('id', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### k-Nearest Neighbors\n",
    "\n",
    "The k-NN algorithm is arguably the simplest machine learning algorithm. Building the model consists only of storing the training dataset. To make a prediction for a new data point, the algorithm finds the closest data points in the training dataset—its “nearest neighbors.”"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let’s investigate whether we can confirm the connection between model complexity and accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(cancer.loc[:, cancer.columns != 'diagnosis'], cancer['diagnosis'], stratify=cancer['diagnosis'], random_state=66)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8aOy5AKzbU8TLXzpDVCuqaxh1bjS3jkhkXP8ujbo3IlAsz8zjt/M3s2V/Mem9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a1bb78fe48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "training_accuracy = []\n",
    "test_accuracy = []\n",
    "# try n_neighbors from 1 to 10\n",
    "neighbors_settings = range(1, 11)\n",
    "\n",
    "for n_neighbors in neighbors_settings:\n",
    "    # build the model\n",
    "    knn = KNeighborsClassifier(n_neighbors=n_neighbors)\n",
    "    knn.fit(X_train, y_train)\n",
    "    # record training set accuracy\n",
    "    training_accuracy.append(knn.score(X_train, y_train))\n",
    "    # record test set accuracy\n",
    "    test_accuracy.append(knn.score(X_test, y_test))\n",
    "\n",
    "plt.plot(neighbors_settings, training_accuracy, label=\"training accuracy\")\n",
    "plt.plot(neighbors_settings, test_accuracy, label=\"test accuracy\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.xlabel(\"n_neighbors\")\n",
    "plt.legend()\n",
    "plt.savefig('knn_compare_model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot shows the training and test set accuracy on the y-axis against the setting of n_neighbors on the x-axis. Considering a single nearest neighbor, the prediction on the training set is perfect. But when more neighbors are considered, the training accuracy drops, indicating that using the single nearest neighbor leads to a model that is too complex.\n",
    "\n",
    "The best performance is somewhere around three neighbors. Still, it is good to keep the scale of the plot in mind. The worst performance is more than 90% accuracy, which might still be pretty good."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot suggests that we should shoose n_neighbors=3. Here we are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of K-NN classifier on training set: 0.96\n",
      "Accuracy of K-NN classifier on test set: 0.92\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "print('Accuracy of K-NN classifier on training set: {:.2f}'.format(knn.score(X_train, y_train)))\n",
    "print('Accuracy of K-NN classifier on test set: {:.2f}'.format(knn.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression\n",
    "\n",
    "One of the most common linear classification algorithms is logistic regression. Despite its name, LogisticRegression is a classification algorithm and not a regression algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 0.955\n",
      "Test set score: 0.937\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logreg = LogisticRegression().fit(X_train, y_train)\n",
    "print(\"Training set score: {:.3f}\".format(logreg.score(X_train, y_train)))\n",
    "print(\"Test set score: {:.3f}\".format(logreg.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default value of C=1 provides quite good performance, with 96% accuracy on training and 0.94 accuracy on test set. Let’s try to increase C to fit a more flexible model to see whether we can improve the performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 0.969\n",
      "Test set score: 0.972\n"
     ]
    }
   ],
   "source": [
    "logreg100 = LogisticRegression(C=100).fit(X_train, y_train)\n",
    "print(\"Training set score: {:.3f}\".format(logreg100.score(X_train, y_train)))\n",
    "print(\"Test set score: {:.3f}\".format(logreg100.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using C=100 results in higher accuracy on both training set and test set, confirming that less regularization and a more complex model should perform better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 0.948\n",
      "Test set score: 0.895\n"
     ]
    }
   ],
   "source": [
    "logreg001 = LogisticRegression(C=0.01).fit(X_train, y_train)\n",
    "print(\"Training set score: {:.3f}\".format(logreg001.score(X_train, y_train)))\n",
    "print(\"Test set score: {:.3f}\".format(logreg001.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using C=0.01 results in lower accuracy on the training set and much lower accuracy on the test set, indicates our model doesn’t generalize well from our training data to unseen data. With C=0.01, overfitting occurs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KNxHdNncp6/54BlPWzuTW3pPpddhlmRzH2NOknSvgtXde48z7z+TS/S9NVPNp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\nH9jk1YiS52OWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a1bb993470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cancer_features = [x for i,x in enumerate(cancer.columns) if i!=0]\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.plot(logreg.coef_.T, 'o', label=\"C=1\")\n",
    "plt.plot(logreg100.coef_.T, '^', label=\"C=100\")\n",
    "plt.plot(logreg001.coef_.T, 'v', label=\"C=0.001\")\n",
    "plt.xticks(range(cancer.shape[1]), cancer_features, rotation=90)\n",
    "plt.hlines(0, 0, cancer.shape[1])\n",
    "plt.ylim(-5, 5)\n",
    "plt.xlabel(\"Feature\")\n",
    "plt.ylabel(\"Coefficient magnitude\")\n",
    "plt.legend()\n",
    "plt.savefig('log_coef')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 1.000\n",
      "Accuracy on test set: 0.937\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "tree = DecisionTreeClassifier(random_state=0)\n",
    "tree.fit(X_train, y_train)\n",
    "print(\"Accuracy on training set: {:.3f}\".format(tree.score(X_train, y_train)))\n",
    "print(\"Accuracy on test set: {:.3f}\".format(tree.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.986\n",
      "Accuracy on test set: 0.937\n"
     ]
    }
   ],
   "source": [
    "tree = DecisionTreeClassifier(max_depth=4, random_state=0)\n",
    "tree.fit(X_train, y_train)\n",
    "\n",
    "print(\"Accuracy on training set: {:.3f}\".format(tree.score(X_train, y_train)))\n",
    "print(\"Accuracy on test set: {:.3f}\".format(tree.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature importance in trees\n",
    "feature importance rates how important each feature is for the decision a tree makes. It is a number between 0 and 1 for each feature, where 0 means “not used at all” and 1 means “perfectly predicts the target.” The feature importances always sum to 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature importances:\n",
      "[ 0.          0.02665433  0.          0.02299068  0.          0.          0.\n",
      "  0.01581361  0.          0.          0.          0.          0.01644953\n",
      "  0.          0.          0.          0.          0.          0.          0.\n",
      "  0.          0.03377907  0.76882622  0.          0.00852939  0.01013634\n",
      "  0.          0.09682083  0.          0.        ]\n"
     ]
    }
   ],
   "source": [
    "print(\"Feature importances:\\n{}\".format(tree.feature_importances_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then visualize the feature importances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cSinGDbAG8EKNfnZpcFx1/cilRMQ7kv6UQdzHgXMptSF7AROz7mPfiBifp1wJ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\nMzMzsz72/wH2EHMKU2GvAAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a1bba1aef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_feature_importances_cancer(model):\n",
    "    plt.figure(figsize=(8,6))\n",
    "    n_features = 30\n",
    "    plt.barh(range(n_features), model.feature_importances_, align='center')\n",
    "    plt.yticks(np.arange(n_features), cancer_features)\n",
    "    plt.xlabel(\"Feature importance\")\n",
    "    plt.ylabel(\"Feature\")\n",
    "    plt.ylim(-1, n_features)\n",
    "\n",
    "plot_feature_importances_cancer(tree)\n",
    "plt.savefig('feature_importance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature perimeter_worst is by far the most important feature. This confirms our observation in analyzing the tree that the first level already separates the two classes fairly well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 1.000\n",
      "Accuracy on test set: 0.958\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier()\n",
    "rf.fit(X_train, y_train)\n",
    "print(\"Accuracy on training set: {:.3f}\".format(rf.score(X_train, y_train)))\n",
    "print(\"Accuracy on test set: {:.3f}\".format(rf.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The random forest gives us an accuracy of 95.8%, better than a single decision tree, without tuning any parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature importance in Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B0bkM++OIuLbOuU64NzMzq8Ehqx1A0veAORFxVnf3ZVkybNiwmDZtWnd3w8zMrEvIIatm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a1bb97c390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importances_cancer(rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly to the single decision tree, the random forest also gives a lot of importance to the “worst radius” feature, but it also chooses “perimeter worst” to be the most informative feature overall. The randomness in building the random forest forces the algorithm to consider many possible explanations, the result being that the random forest captures a much broader picture of the data than a single tree."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 1.000\n",
      "Accuracy on test set: 0.944\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "gb = GradientBoostingClassifier(random_state=0)\n",
    "gb.fit(X_train, y_train)\n",
    "\n",
    "print(\"Accuracy on training set: {:.3f}\".format(gb.score(X_train, y_train)))\n",
    "print(\"Accuracy on test set: {:.3f}\".format(gb.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the training set accuracy is 100%, we are likely to be overfitting. To reduce overfitting, we could either apply stronger pre-pruning by limiting the maximum depth or lower the learning rate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.988\n",
      "Accuracy on test set: 0.937\n"
     ]
    }
   ],
   "source": [
    "gb1 = GradientBoostingClassifier(random_state=0, max_depth=1)\n",
    "gb1.fit(X_train, y_train)\n",
    "\n",
    "print(\"Accuracy on training set: {:.3f}\".format(gb1.score(X_train, y_train)))\n",
    "print(\"Accuracy on test set: {:.3f}\".format(gb1.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.984\n",
      "Accuracy on test set: 0.930\n"
     ]
    }
   ],
   "source": [
    "gb2 = GradientBoostingClassifier(random_state=0, learning_rate=0.01)\n",
    "gb2.fit(X_train, y_train)\n",
    "\n",
    "print(\"Accuracy on training set: {:.3f}\".format(gb2.score(X_train, y_train)))\n",
    "print(\"Accuracy on test set: {:.3f}\".format(gb2.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both methods of decreasing the model complexity reduced the training set accuracy, as expected. In this case, none of these methods increased the generalization performance of the test set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Still, we can visualize the feature importances to get more insight into our model even though we are not really happy with the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pSEnTJE1b8MacGt0zMzPr2fy2IzwfEZPz89WUx3/1aiwCLDbgqHJT/m6hlAp6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\n/vdlxwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a1bbce00f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gb = GradientBoostingClassifier(random_state=0, max_depth=1)\n",
    "gb.fit(X_train, y_train)\n",
    "\n",
    "plot_feature_importances_cancer(gb1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the feature importances of the gradient boosted trees are somewhat similar to the feature importances of the random forests, though the gradient boosting completely ignored some of the features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 1.00\n",
      "Accuracy on test set: 0.63\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svc = SVC()\n",
    "svc.fit(X_train, y_train)\n",
    "\n",
    "print(\"Accuracy on training set: {:.2f}\".format(svc.score(X_train, y_train)))\n",
    "print(\"Accuracy on test set: {:.2f}\".format(svc.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model overfits quite substantially, with a perfect score on the training set and only 63% accuracy on the test set.\n",
    "\n",
    "SVM requires all the features to vary on a similar scale. We will need to rescale our data that all the features are approximately on the same scale:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.95\n",
      "Accuracy on test set: 0.94\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.fit_transform(X_test)\n",
    "\n",
    "svc = SVC()\n",
    "svc.fit(X_train_scaled, y_train)\n",
    "\n",
    "print(\"Accuracy on training set: {:.2f}\".format(svc.score(X_train_scaled, y_train)))\n",
    "print(\"Accuracy on test set: {:.2f}\".format(svc.score(X_test_scaled, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scaling the data made a huge difference! Now we are actually in an underfitting regime, where training and test set performance are quite similar but less close to 100% accuracy. From here, we can try increasing either C or gamma to fit a more complex model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.986\n",
      "Accuracy on test set: 0.965\n"
     ]
    }
   ],
   "source": [
    "svc = SVC(C=1000)\n",
    "svc.fit(X_train_scaled, y_train)\n",
    "\n",
    "print(\"Accuracy on training set: {:.3f}\".format(\n",
    "    svc.score(X_train_scaled, y_train)))\n",
    "print(\"Accuracy on test set: {:.3f}\".format(svc.score(X_test_scaled, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, increasing C allows us to improve the model significantly, resulting in 96.5% test set accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neural Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.63\n",
      "Accuracy on test set: 0.63\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "mlp = MLPClassifier(random_state=42)\n",
    "mlp.fit(X_train, y_train)\n",
    "\n",
    "print(\"Accuracy on training set: {:.2f}\".format(mlp.score(X_train, y_train)))\n",
    "print(\"Accuracy on test set: {:.2f}\".format(mlp.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is likely due to scaling of the data. Neural networks also expect all input features to vary in a similar way, and ideally to have a mean of 0, and a variance of 1. We must rescale our data so that it fulfills these requirements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.962\n",
      "Accuracy on test set: 0.958\n"
     ]
    }
   ],
   "source": [
    "scaler = MinMaxScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.fit_transform(X_test)\n",
    "\n",
    "mlp = MLPClassifier(random_state=0)\n",
    "mlp.fit(X_train_scaled, y_train)\n",
    "\n",
    "print(\"Accuracy on training set: {:.3f}\".format(\n",
    "    mlp.score(X_train_scaled, y_train)))\n",
    "print(\"Accuracy on test set: {:.3f}\".format(mlp.score(X_test_scaled, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results are much better after scaling, and already quite competitive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.958\n",
      "Accuracy on test set: 0.979\n"
     ]
    }
   ],
   "source": [
    "mlp = MLPClassifier(max_iter=1000, alpha=1, random_state=0)\n",
    "mlp.fit(X_train, y_train)\n",
    "\n",
    "print(\"Accuracy on training set: {:.3f}\".format(\n",
    "    mlp.score(X_train, y_train)))\n",
    "print(\"Accuracy on test set: {:.3f}\".format(mlp.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1e2bd0e3898>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H2gzWrPWPcUuDX5ld2ZTj4dvqF3rNOv/aUVWOyvUcxdlpYGPZ9S9OH+SmsfqwHPMV5Cju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\nnS9puqQ5kv4DQNLB4XMfSdWS5ksaHZY9LCx7rqT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ih4Ha98VHI11XXxGNqXiqJhqzdp/4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VwFjAYeAD4VEVWnWdmgu4/1FEn7AwcDe0fELsCDwECgPiLahvEXxZTxE9Nr54h4b1o3BTgpInYG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\nbb5DSgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e2bd08fa20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 5))\n",
    "plt.imshow(mlp.coefs_[0], interpolation='none', cmap='viridis')\n",
    "plt.yticks(range(30), cancer_features)\n",
    "plt.xlabel(\"Columns in weight matrix\")\n",
    "plt.ylabel(\"Input feature\")\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot shows the weights that were learned connecting the input to the first hidden layer. The rows in this plot correspond to the 30 input features, while the columns correspond to the 100 hidden units. Light colors represent large positive values, while dark colors represent negative values.\n",
    "\n",
    "One possible inference we can make is that features that have very small weights for all of the hidden units are “less important” to the model. We can see that “mean smoothness” and “mean compactness,” in addition to the features found between “smoothness error” and “fractal dimension error,” have relatively low weights compared to other features. This could mean that these are less important features or possibly that we didn’t represent them in a way that the neural network could use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}