Ensemble ML Techniques.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "13b23e69",
   "metadata": {},
   "source": [
    "## Ensemble Machine Learning\n",
    "----\n",
    "Ensemble machine learning refers to the technique of combining multiple individual machine learning models to make more accurate predictions or classifications. Instead of relying on a single model, ensemble methods leverage the wisdom of the crowd by aggregating predictions from multiple models, often resulting in better performance and increased robustness.\n",
    "\n",
    "**There are several popular ensemble methods in machine learning, including:**\n",
    "- Bagging\n",
    "- Random Forest\n",
    "- Boosting\n",
    "- Gradient Boosting\n",
    "- Stacking\n",
    "\n",
    "Ensemble methods can improve the accuracy and robustness of machine learning models, especially when individual models have different strengths and weaknesses. They are widely used in various domains and have proven effective in many real-world applications."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ac8c815f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings as wr\n",
    "wr.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "84753fa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import make_moons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b156d00a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = make_moons(n_samples=500, noise=0.30, random_state=42)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fbbce25b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "578db527",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_clf = LogisticRegression(solver=\"liblinear\", random_state=42)\n",
    "rnd_clf = RandomForestClassifier(n_estimators=10, random_state=42)\n",
    "svm_clf = SVC(gamma=\"auto\", random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "af984ce6",
   "metadata": {},
   "outputs": [],
   "source": [
    "voting_clf = VotingClassifier(\n",
    "    estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n",
    "    voting='hard')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "67391773",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VotingClassifier(estimators=[(&#x27;lr&#x27;,\n",
       "                              LogisticRegression(random_state=42,\n",
       "                                                 solver=&#x27;liblinear&#x27;)),\n",
       "                             (&#x27;rf&#x27;,\n",
       "                              RandomForestClassifier(n_estimators=10,\n",
       "                                                     random_state=42)),\n",
       "                             (&#x27;svc&#x27;, SVC(gamma=&#x27;auto&#x27;, random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">VotingClassifier</label><div class=\"sk-toggleable__content\"><pre>VotingClassifier(estimators=[(&#x27;lr&#x27;,\n",
       "                              LogisticRegression(random_state=42,\n",
       "                                                 solver=&#x27;liblinear&#x27;)),\n",
       "                             (&#x27;rf&#x27;,\n",
       "                              RandomForestClassifier(n_estimators=10,\n",
       "                                                     random_state=42)),\n",
       "                             (&#x27;svc&#x27;, SVC(gamma=&#x27;auto&#x27;, random_state=42))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>lr</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=42, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>rf</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=10, random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>svc</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(gamma=&#x27;auto&#x27;, random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "VotingClassifier(estimators=[('lr',\n",
       "                              LogisticRegression(random_state=42,\n",
       "                                                 solver='liblinear')),\n",
       "                             ('rf',\n",
       "                              RandomForestClassifier(n_estimators=10,\n",
       "                                                     random_state=42)),\n",
       "                             ('svc', SVC(gamma='auto', random_state=42))])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cd2b4f0e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression 86 %\n",
      "RandomForestClassifier 87 %\n",
      "SVC 89 %\n",
      "VotingClassifier 90 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "for clf in (log_clf, rnd_clf, svm_clf, voting_clf):\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print(clf.__class__.__name__, round(accuracy_score(y_test, y_pred)*100), \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dbc5c9e",
   "metadata": {},
   "source": [
    "### Based on Class Probabilities from each classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e83e3aeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "                              LogisticRegression(random_state=42,\n",
       "                                                 solver=&#x27;liblinear&#x27;)),\n",
       "                             (&#x27;rf&#x27;,\n",
       "                              RandomForestClassifier(n_estimators=10,\n",
       "                                                     random_state=42)),\n",
       "                             (&#x27;svc&#x27;,\n",
       "                              SVC(gamma=&#x27;auto&#x27;, probability=True,\n",
       "                                  random_state=42))],\n",
       "                 voting=&#x27;soft&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">VotingClassifier</label><div class=\"sk-toggleable__content\"><pre>VotingClassifier(estimators=[(&#x27;lr&#x27;,\n",
       "                              LogisticRegression(random_state=42,\n",
       "                                                 solver=&#x27;liblinear&#x27;)),\n",
       "                             (&#x27;rf&#x27;,\n",
       "                              RandomForestClassifier(n_estimators=10,\n",
       "                                                     random_state=42)),\n",
       "                             (&#x27;svc&#x27;,\n",
       "                              SVC(gamma=&#x27;auto&#x27;, probability=True,\n",
       "                                  random_state=42))],\n",
       "                 voting=&#x27;soft&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>lr</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=42, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>rf</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=10, random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>svc</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(gamma=&#x27;auto&#x27;, probability=True, random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "VotingClassifier(estimators=[('lr',\n",
       "                              LogisticRegression(random_state=42,\n",
       "                                                 solver='liblinear')),\n",
       "                             ('rf',\n",
       "                              RandomForestClassifier(n_estimators=10,\n",
       "                                                     random_state=42)),\n",
       "                             ('svc',\n",
       "                              SVC(gamma='auto', probability=True,\n",
       "                                  random_state=42))],\n",
       "                 voting='soft')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_clf = LogisticRegression(solver=\"liblinear\", random_state=42)\n",
    "rnd_clf = RandomForestClassifier(n_estimators=10, random_state=42)\n",
    "svm_clf = SVC(gamma=\"auto\", probability=True, random_state=42)\n",
    "\n",
    "voting_clf = VotingClassifier(\n",
    "    estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n",
    "    voting='soft') # Here voting solf means all the classifiers can estimate class probabilities\n",
    "\n",
    "voting_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8b23980d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression 86 %\n",
      "RandomForestClassifier 87 %\n",
      "SVC 89 %\n",
      "VotingClassifier 91 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "for clf in (log_clf, rnd_clf, svm_clf, voting_clf):\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print(clf.__class__.__name__, round(accuracy_score(y_test, y_pred)*100), \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85a26058",
   "metadata": {},
   "source": [
    "## Bagging Ensemble ML\n",
    "----\n",
    "Bagging (Bootstrap Aggregating) is an ensemble machine learning technique that combines multiple individual models trained on different subsets of the training data to make predictions. The main idea behind bagging is to introduce randomness in the training process by creating multiple subsets of the original data through resampling, and training each model on a different subset.\n",
    "\n",
    "**Here's how the bagging ensemble technique works:**\n",
    "- **Bootstrap Sampling:** Bagging starts by creating multiple subsets of the training data through bootstrap sampling. Bootstrap sampling involves randomly selecting data points from the original dataset with replacement, which means that some data points may be selected multiple times, while others may not be selected at all. Each subset, also known as a bootstrap sample, has the same size as the original dataset but may differ in terms of the included data points.\n",
    "\n",
    "\n",
    "- **Model Training:** Once the bootstrap samples are created, an individual model, often referred to as a base model or weak learner, is trained on each bootstrap sample. The base model can be any machine learning algorithm capable of making predictions or classifications. The same algorithm is typically used for each base model, although slight variations can be introduced.\n",
    "- **Prediction Aggregation:** After training the base models, the bagging ensemble combines their predictions to make the final prediction. The aggregation can be done by averaging the predictions (for regression problems) or by majority voting (for classification problems) from all the base models. Aggregating the predictions helps to reduce the variance and improve the generalization of the ensemble model.\n",
    "\n",
    "The key benefits of bagging are that it reduces overfitting, increases stability, and can improve prediction accuracy. By training models on different subsets of the data, bagging allows each model to learn from slightly different perspectives, which helps to capture different aspects of the data and reduce the impact of noisy or outlier data points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "72259689",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "bag_clf = BaggingClassifier(\n",
    "    DecisionTreeClassifier(random_state=42), n_estimators=500,\n",
    "    max_samples=100, bootstrap=True, n_jobs=-1, random_state=42)\n",
    "bag_clf.fit(X_train, y_train) #boostrap = True means it is applying Bagging and False means Pasting technique\n",
    "y_pred = bag_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7ae1887d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 90%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(\"Accuracy: \" + str(round(accuracy_score(y_test, y_pred)*100)) + \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e6a91fdd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 86%\n"
     ]
    }
   ],
   "source": [
    "tree_clf = DecisionTreeClassifier(random_state=42)\n",
    "tree_clf.fit(X_train, y_train)\n",
    "y_pred_tree = tree_clf.predict(X_test)\n",
    "print(\"Accuracy: \" + str(round(accuracy_score(y_test, y_pred_tree)*100)) + \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e74bf41a",
   "metadata": {},
   "source": [
    "***So, here we can see that Bagging performs better than normal Decision Tree***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b0e03296",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "def plot_decision_boundary(clf, X, y, axes=[-1.5, 2.5, -1, 1.5], alpha=0.5, contour=True):\n",
    "    x1s = np.linspace(axes[0], axes[1], 100)\n",
    "    x2s = np.linspace(axes[2], axes[3], 100)\n",
    "    x1, x2 = np.meshgrid(x1s, x2s)\n",
    "    X_new = np.c_[x1.ravel(), x2.ravel()]\n",
    "    y_pred = clf.predict(X_new).reshape(x1.shape)\n",
    "    custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\n",
    "    plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap)\n",
    "    if contour:\n",
    "        custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])\n",
    "        plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\n",
    "    plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", alpha=alpha)\n",
    "    plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", alpha=alpha)\n",
    "    plt.axis(axes)\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=18)\n",
    "    plt.ylabel(r\"$x_2$\", fontsize=18, rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b4c0bd8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Decision Trees with Bagging')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,4))\n",
    "plt.subplot(121)\n",
    "plot_decision_boundary(tree_clf, X, y)\n",
    "plt.title(\"Decision Tree\", fontsize=14)\n",
    "plt.subplot(122)\n",
    "plot_decision_boundary(bag_clf, X, y)\n",
    "plt.title(\"Decision Trees with Bagging\", fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d0b3ad2",
   "metadata": {},
   "source": [
    "### Out of Bag Evaluation (Bagging)\n",
    "\n",
    "In bagging, the out-of-bag (OOB) evaluation is a technique used to estimate the performance of a bagged ensemble model without the need for cross-validation or a separate validation set. It provides a way to assess the model's accuracy by utilizing the samples that were not selected during the training process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c815a4f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a BaggingClassifier with your base model and desired parameters\n",
    "bag_clf = BaggingClassifier( DecisionTreeClassifier(), n_estimators=500,\n",
    "                             bootstrap = True, n_jobs = -1, oob_score = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b7fb5ce5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9013333333333333"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the bagging model\n",
    "bag_clf.fit(X_train, y_train)\n",
    "bag_clf.oob_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6211e545",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out of the Bag Score: 90.13 %\n"
     ]
    }
   ],
   "source": [
    "# Access the OOB score\n",
    "oob_score = bag_clf.oob_score_\n",
    "\n",
    "print(\"Out of the Bag Score:\", round(oob_score*100,2),\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5784e302",
   "metadata": {},
   "source": [
    "#### According to the OOB evaluation =, this Bagging Classifier is likely to achive about 89.33% accuracy on the test set. Let's verify this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5b1b6f6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out of the Bag Score: 92.0 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "y_pred = bag_clf.predict(X_test)\n",
    "\n",
    "acc = accuracy_score(y_test, y_pred)\n",
    "\n",
    "print(\"Out of the Bag Score:\", round(acc*100,2),\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b102d40",
   "metadata": {},
   "source": [
    "##### We get 90.4% which is close enough!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a887597",
   "metadata": {},
   "source": [
    "## Random Forests\n",
    "-----\n",
    "\n",
    "Random Forest is an ensemble learning method that combines multiple decision trees to create a more robust and accurate model. It is a popular algorithm for both classification and regression tasks.\n",
    "\n",
    "**Here are the key characteristics and steps involved in the Random Forest algorithm:**\n",
    "\n",
    "- **Ensemble of Decision Trees:** Random Forest is composed of a collection of decision trees. Each decision tree is built independently using a random subset of the training data.\n",
    "\n",
    "- **Random Feature Subsets:** For each tree in the Random Forest, a random subset of features is selected as candidates for splitting at each node. This random feature selection helps to decorrelate the trees and reduce overfitting.\n",
    "\n",
    "- **Bootstrap Aggregating (Bagging):** Each decision tree in the Random Forest is trained on a bootstrap sample of the training data. Bootstrap sampling involves randomly selecting samples from the training data with replacement, allowing some samples to be selected multiple times and others not to be selected at all. This technique creates different training sets for each tree and introduces diversity in the ensemble.\n",
    "\n",
    "- **Voting or Averaging:** For classification tasks, the Random Forest predicts the class label by majority voting among the individual trees. For regression tasks, the Random Forest predicts the output value by averaging the predictions from all the trees.\n",
    "\n",
    "- **Out-of-Bag (OOB) Evaluation:** Random Forest provides an OOB evaluation technique. Since each tree is trained on a bootstrap sample, there will be a set of samples (approximately one-third of the data) that are not included in the training of a particular tree. These out-of-bag samples can be used to evaluate the model's performance without the need for a separate validation set.\n",
    "\n",
    "**Random Forests offer several benefits:**\n",
    "\n",
    "- Robustness\n",
    "- Non-linearity\n",
    "- Feature Importance\n",
    "- Overfitting Reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "55f0bfa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "bag_clf = BaggingClassifier(\n",
    "    DecisionTreeClassifier(splitter=\"random\", max_leaf_nodes=16, random_state=42),\n",
    "    n_estimators=500, max_samples=1.0, bootstrap=True, n_jobs=-1, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a700e50",
   "metadata": {},
   "source": [
    "***Here we used the BaggingClassifier to work it same as Random Forests algorithm using its parameters***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "089efa78",
   "metadata": {},
   "outputs": [],
   "source": [
    "bag_clf.fit(X_train, y_train)\n",
    "y_pred = bag_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "49602d9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.92\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9dda68fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16, n_jobs=-1, random_state=42)\n",
    "rnd_clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred_rf = rnd_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1ed1d65d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.912\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred_rf)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3c4660c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.976"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(y_pred == y_pred_rf) / len(y_pred)  # almost identical predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9b848aa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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