Ensemble-Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensemble Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ensemble Learning in machine learning is a technique where multiple models, often of the same or different types, are combined to improve the overall performance of the system. The basic idea is that by combining several weak learners, one can create a stronger learner that generalizes better and is more robust. There are two main ensemble learning techniques.\n",
    "1.   <b>Boosting</b>: Boosting is an ensemble technique where a sequence of models are trained, and each subsequent model corrects the errors of the previous ones. Ex: AdaBoost (Adaptive Boosting), Gradient Boosting\n",
    "2.   <b>Bagging</b>: Bagging involves training multiple instances of the same model on different subsets of the training data. The predictions from each model are then averaged or voted upon to make the final prediction. Ex: Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer, load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier, RandomForestClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AdaBoost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "For more information on the AdaBoost, see the document [here](https://www.cs.toronto.edu/~mbrubake/teaching/C11/Handouts/AdaBoost.pdf) and the YouTube video explaining it [here](https://youtu.be/LsK-xG1cLYA?si=cDAdJlwJasNo2D0Z). First let's used iris dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_dataset = load_iris()\n",
    "\n",
    "X = iris_dataset.data\n",
    "y = iris_dataset.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_ada = AdaBoostClassifier()\n",
    "\n",
    "iris_ada.fit(X_train, y_train)\n",
    "y_pred = iris_ada.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        19\n",
      "           1       1.00      1.00      1.00        13\n",
      "           2       1.00      1.00      1.00        13\n",
      "\n",
      "    accuracy                           1.00        45\n",
      "   macro avg       1.00      1.00      1.00        45\n",
      "weighted avg       1.00      1.00      1.00        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(iris_ada.score(X_test, y_test))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "disp = ConfusionMatrixDisplay(cm)\n",
    "disp.plot()\n",
    "disp.figure_.set_size_inches(w=5, h=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use Diagnostic Wisconsin Breast Cancer dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer_dataset = load_breast_cancer()\n",
    "\n",
    "X = cancer_dataset.data\n",
    "y = cancer_dataset.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer_ada = AdaBoostClassifier()\n",
    "\n",
    "cancer_ada.fit(X_train, y_train)\n",
    "y_pred = cancer_ada.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9766081871345029\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.97      0.97        63\n",
      "           1       0.98      0.98      0.98       108\n",
      "\n",
      "    accuracy                           0.98       171\n",
      "   macro avg       0.97      0.97      0.97       171\n",
      "weighted avg       0.98      0.98      0.98       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(cancer_ada.score(X_test, y_test))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZbDbV1NTUJR4AADx2QUnN6XTWdxwAgPpg2MzPkflxpVan+9QqKiq8FQcAoB6cnlMze/gr00mtpqZGjz76qC666CI1a9ZMBw4ckCTNmDFDS5Ys8XqAAIA6MDw8/JTppDZ37lwtX75c8+fPV2hoqKv98ssv1yuvvOLV4AAAdXN6oYjZw1+ZTmorVqzQSy+9pGHDhik4ONjV3rVrV33xxRdeDQ4A4AUBUqVJHiS1w4cPKyUl5Yx2p9Op6upqrwQFAIAnTCe1zp07a8uWLWe0v/nmm+revbtXggIAeEegDT+a3lFk5syZysjI0OHDh+V0OvXWW28pLy9PK1as0Lp16+ojRgCApwJs70fTldqgQYO0du1a/fOf/1RERIRmzpypPXv2aO3atbrhhhvqI0YAgMdsHh7mHD58WHfddZdatGih8PBwdenSRTt37nSdNwxDM2fOVHx8vMLDw5WWlqa9e/fW8budyaO9H/v27asNGzZ4OxYAgLc1QKX2/fff65prrlH//v31j3/8Q61atdLevXvVvHlzV5/58+crOztbr776qpKTkzVjxgylp6fr888/V1hYmMkAz83jDY137typPXv2SKqdZ+vRo4fXggIAeEkDJLUnnnhCiYmJWrZsmastOTn5p7czDC1cuFAPP/ywBg0aJKl2JX1sbKzWrFmjoUOHmgzw3EwPPx46dEh9+/bVVVddpQkTJmjChAnq1auX+vTpo0OHDnktMACAb5WWlrodlZWVZ+33zjvvqGfPnvr973+v1q1bq3v37nr55Zdd5/Pz81VYWKi0tDRXm8PhUO/evbV9+3avxmw6qd17772qrq7Wnj17VFxcrOLiYu3Zs0dOp1P33nuvV4MDANTR6b0fzR6SEhMT5XA4XEdWVtZZP+LAgQNatGiR2rdvr/fee0+jR4/W+PHj9eqrr0qSCgsLJUmxsbFu18XGxrrOeYvp4cdNmzZp27Zt6tChg6utQ4cOeu6559S3b1+vBgcAqJu6PE+toKBAUVFRrna73X7W/k6nUz179tS8efMkSd27d9dnn32mxYsXKyMjw6O4PWW6UktMTDzrTdY1NTVKSEjwSlAAAC+pw96PUVFRbse5klp8fLw6d+7s1tapUycdPHhQkhQXFydJKioqcutTVFTkOuctppPak08+qXHjxrkt1dy5c6cmTJigp556yqvBAQDqqA7DjxfqmmuuUV5enlvbl19+qbZt20qqXTQSFxen3Nxc1/nS0lLt2LFDqampdf+OP3NBw4/NmzeXzfbTlywvL1fv3r3VpEnt5adOnVKTJk10zz33XPADRQEA9c9m1B5mrzFj0qRJuvrqqzVv3jzdcccd+vDDD/XSSy/ppZdeqn0/m00TJ07UY489pvbt27uW9CckJHg9Z1xQUlu4cKFXPxQAYB29evXS22+/renTp2vOnDlKTk7WwoULNWzYMFefqVOnqry8XPfdd5+OHz+uPn36aP369V69R026wKTW0BN9AAAvaaBtsm699Vbdeuut5zxvs9k0Z84czZkzx/ybm+DxzddS7ZOvq6qq3Np+vlIGAOBjHsyRme7fiJheKFJeXq6xY8eqdevWioiIUPPmzd0OAEAjwpOvz2/q1Kl6//33tWjRItntdr3yyiuaPXu2EhIStGLFivqIEQDgqQBLaqaHH9euXasVK1aoX79+GjFihPr27auUlBS1bdtWK1eudJsYBACgIZmu1IqLi9WuXTtJtfNnxcXFkqQ+ffpo8+bN3o0OAFA3AVapmU5q7dq1U35+viSpY8eOWr16taTaCi46OtqrwQEA6qgBbr5uTEwntREjRuiTTz6RJE2bNk05OTkKCwvTpEmTNGXKFK8HCADw3Ombr80e/sr0nNqkSZNc/z0tLU1ffPGFdu3apZSUFF1xxRVeDQ4AUEcNdJ9aY1Gn+9QkqW3btq79vQAA8KULSmrZ2dkX/Ibjx4/3OBgAAOrigpLaggULLujNbDYbSQ0AGhGbPNjQuF4iaRgXlNROr3ZsrFJG71YTW4ivw4CFvXdkt69DgMWVnnCq+aX18MYBtk1WnefUAACNGAtFAACWQVIDAFhFQzwktDExffM1AACNFZUaAFhZgA0/elSpbdmyRXfddZdSU1N1+PBhSdKf//xnbd261avBAQDqiA2Nz++vf/2r0tPTFR4ero8//liVlZWSpJKSEs2bN8/rAQIAPBdoez+aTmqPPfaYFi9erJdfflkhIT/dG3bNNdfoo48+8mpwAIA6CrBd+k3PqeXl5enaa689o93hcOj48ePeiAkA4C3MqZ1fXFyc9u3bd0b71q1bXQ8PBQDAF0wntVGjRmnChAnasWOHbDabjhw5opUrV2ry5MkaPXp0fcQIAPBQoM2pmR5+nDZtmpxOp66//nqdPHlS1157rex2uyZPnqxx48bVR4wAAE8F2PCj6aRms9n00EMPacqUKdq3b5/KysrUuXNnNWvWrD7iAwDUhSeVVyAltdNCQ0PVuXNnb8YCAPA2KrXz69+/v2y2cy/3fP/99+sUEADAi0hq59etWze319XV1dq9e7c+++wzZWRkeCsuAABMM53UzvUU7EceeURlZWV1DggA4D3s0u+hu+66S0uXLvXW2wEAYJrXdunfvn27wsLCvPV2AABvYE7t/G677Ta314Zh6OjRo9q5c6dmzJjhtcAAAHUXaMOPppOaw+Fwex0UFKQOHTpozpw5uvHGG70WGADAS/w4SZllKqnV1NRoxIgR6tKli5o3b15fMQEA4BFTC0WCg4N14403shs/APgLHhJ6fpdffrkOHDhQH7EAALws0DY09ughoZMnT9a6det09OhRlZaWuh0AgEYkwCq1C55TmzNnjv70pz/p5ptvliT97ne/c9suyzAM2Ww21dTUeD9KAIBHWP14DrNnz9b999+vf/3rX/UZDwDAm7hP7ewMo/ZbXnfddfUWDAAAdWFqSf/5ducHADRCVGrndumll/5qYisuLq5TQAAA72FO7Txmz559xo4iAIBGjErt3IYOHarWrVvXVywAAG8jqZ0d82kA4H8Cbfjxgm++Pr36EQCAxuqCKzWn01mfcQAA6gPDjwAAq2D4EQBgHQ289+Pjjz8um82miRMnutoqKio0ZswYtWjRQs2aNdOQIUNUVFTk+YecB0kNAKysAZPav//9b7344ou64oor3NonTZqktWvX6o033tCmTZt05MgR3XbbbZ59yK8gqQGAhdk8PMwqKyvTsGHD9PLLL7s9RLqkpERLlizRM888o9/+9rfq0aOHli1bpm3btumDDz6o03c7G5IaAOCsfvloscrKynP2HTNmjG655RalpaW5te/atUvV1dVu7R07dlRSUpK2b9/u9ZhJagBgZXUYfkxMTJTD4XAdWVlZZ/2I1157TR999NFZzxcWFio0NFTR0dFu7bGxsSosLPTCF3TH6kcAsLC6rH4sKChQVFSUq91ut5/Rt6CgQBMmTNCGDRsUFhZWl1C9gkoNAKysDpVaVFSU23G2pLZr1y4dO3ZMV155pZo0aaImTZpo06ZNys7OVpMmTRQbG6uqqiodP37c7bqioiLFxcV5/etSqQGA1dXjfWfXX3+9Pv30U7e2ESNGqGPHjnrwwQeVmJiokJAQ5ebmasiQIZKkvLw8HTx4UKmpqV6Ph6QGABZW3zdfR0ZG6vLLL3dri4iIUIsWLVztI0eOVGZmpmJiYhQVFaVx48YpNTVVv/nNb8wFdgFIagCAerVgwQIFBQVpyJAhqqysVHp6ul544YV6+SySGgBYmQ/2fty4caPb67CwMOXk5CgnJ6dub3wBSGoAYGGBtvcjSQ0ArIxd+gEAVkGlBgCwjgCr1Lj5GgBgGVRqAGBlAVapkdQAwMKYUwMAWAeVGgDAKmyGIZthLkuZ7d+YkNQAwMoCrFJj9SMAwDKo1ADAwlgoAgCwjgAbfiSpAYCFUakBAKyDSg0AYBWBVqmx+hEAYBlUagBgZQw/AgCsxJ+HE80iqQGAlRlG7WH2Gj9FUgMAC2OhCAAAfopKDQCsjIUisKI7xxbpmptLlJhSqaqKIH2+s6mWzI3Xof1hvg4NfuLTDyL0xguttffTpiouCtGsJfm6ekCJ67xhSCuejNP6VS1UVhqszj3LNf7xAl3UrsrtfXb8M0orF8Qqf0+4Qu1OdflNuR5Zlt/QXydg2Jy1h9lr/BXDjwHiitRyrV3eUhNvba/pQ9spuImheX85IHt4ja9Dg5+oOBmkdpf9oLHzDp31/Oqc1vrb0lYa93iBnl33pcKaOvV//3iJqipsrj5b/u7Q/PFJuvHOYi3akKdn/rZX/f/r+4b6CoHJ8PDwUz5Naps3b9bAgQOVkJAgm82mNWvW+DIcS3toWDttWB2jr78M04HPw/X0xCTFtqlW+yt+8HVo8BO9fntCwx8s1DU/q85OMwxpzSut9IcJhbr6plK161yhqdlf67uiEG1b75Ak1ZySFs+8SKMePqJb7/5ObS6pVNtLK3Xd74438DcJLKcXipg9/JVPk1p5ebm6du2qnJwcX4YRkCKiaiu0E8eDfRwJrKDwYKiKj4Xoyr5lrraIKKc6dj+pPbsiJEl7P22qb4+GyhYkPXDDpfpDt8v00LB2+uoLhsDr1ekl/WYPP+XTObUBAwZowIABvgwhINlshu6ffViffdhUX+eF+zocWEDxsdqfkuhW1W7t0a2qXecKvw6VJP2/p+N03yOHFZdYpTcXt9aUISlasnWPopozFI6686s5tcrKSpWWlrodMG/svMNq27FCWaPb+joUBBDnj4sP/jChSH1vKVH7K37QnxYclM0mbVkX7dPYrIzhx0YsKytLDofDdSQmJvo6JL8zZu4h9b6hVFNvv0TfHg31dTiwiJjWpyRJx78JcWs//k2I61xMbO1/JrWvcJ0PtRuKa1upY4fdr4MXsVCk8Zo+fbpKSkpcR0FBga9D8iOGxsw9pKtvKtHU31+iogK7rwOChcQlVSmmdbU+3trM1VZ+IkhffNxUnXqUS5LaX3FSIXanDu3/6W/vVLVUVBCq2DbVZ7wnvCPQKjW/uk/NbrfLbufH2BNj5x1W///6Xo+MSNYPZUFq/uPcR/mJYFVV+NW/28BHfigP0pH8n/7/V1gQqv2fhSsy+pRat6nW4Hu/0V+ejdVFyZWKS6rSq/Pj1SK2WlffVLtaMiLSqVv+z3f689NxapVQrdZtqvTmotaSpL63HvfFVwoM7P0IKxo4/DtJ0lNv7Xdrf2piojasjvFFSPAzX37SVFNvT3G9fvGRiyRJN9xRrMkLD+qOMcdUcTJIz05NVFlpsC7rVa65Kw8oNOynH8hRMw4rONjQ/PFJqqoIUofuJ/XEG/sVGc0ikfoSaHs/+jSplZWVad++fa7X+fn52r17t2JiYpSUlOTDyKwnPaGrr0OAn+t6dZneO7L7nOdtNiljaqEyphaes0+TEOm+WUd036wj9RAh4OOktnPnTvXv39/1OjMzU5KUkZGh5cuX+ygqALAQ9n5sOP369ZPhx2O3ANDYMfwIALAOp1F7mL3GT5HUAMDKGH4EAFiFTR4MP9ZLJA2DG5QAAJZBpQYAVsbN1wAAq2D1IwDAOlgoAgCwCpthyGZyONFs/8aEpAYAVub88TB7jZ9i9SMAwDKo1ADAwhh+BABYR4AtFGH4EQCs7PR9amYPE7KystSrVy9FRkaqdevWGjx4sPLy8tz6VFRUaMyYMWrRooWaNWumIUOGqKioyJvfVBJJDQAs7fR9amYPMzZt2qQxY8bogw8+0IYNG1RdXa0bb7xR5eXlrj6TJk3S2rVr9cYbb2jTpk06cuSIbrvtNi9/W4YfAcDaGmBHkfXr17u9Xr58uVq3bq1du3bp2muvVUlJiZYsWaJVq1bpt7/9rSRp2bJl6tSpkz744AP95je/MRffeVCpAQDOqrS01O2orKy8oOtKSkokSTExMZKkXbt2qbq6Wmlpaa4+HTt2VFJSkrZv3+7VmElqAGBhNqdnhyQlJibK4XC4jqysrF/9PKfTqYkTJ+qaa67R5ZdfLkkqLCxUaGiooqOj3frGxsaqsLDQq9+X4UcAsLI6DD8WFBQoKirK1Wy323/10jFjxuizzz7T1q1bzX2ml5DUAMDK6rCkPyoqyi2p/ZqxY8dq3bp12rx5s9q0aeNqj4uLU1VVlY4fP+5WrRUVFSkuLs5kcOfH8CMAWNjpm6/NHmYYhqGxY8fq7bff1vvvv6/k5GS38z169FBISIhyc3NdbXl5eTp48KBSU1O98j1Po1IDACtrgNWPY8aM0apVq/S3v/1NkZGRrnkyh8Oh8PBwORwOjRw5UpmZmYqJiVFUVJTGjRun1NRUr658lEhqAIA6WrRokSSpX79+bu3Lli3T8OHDJUkLFixQUFCQhgwZosrKSqWnp+uFF17weiwkNQCwMkPmd903Xdj9+gVhYWHKyclRTk6OyWDMIakBgIWxoTEAwDoMeTCnVi+RNAiSGgBYWQMsFGlMSGoAYGVOSTYPrvFT3KcGALAMKjUAsDAWigAArIM5NQCAZZDUAACWQVIDAFgGqx8BAPBPVGoAYGGsfgQAWAdzagAAy3Aaks1kknKS1AAAjRGVGgDAOjxIan68TT+rHwEAlkGlBgBWxvAjAMAynIZMDyeyUAQA0CgZztrD7DV+iqQGAFbG8CMAwDICbPiR1Y8AAMugUgMAK2P4EQBgGYY8SGr1EkmDIKkBgJVRqQEALMPplOmnfjpZ0g8AaIwCrFJj9SMAwDKo1ADAygKsUiOpAYCVBdjN1yQ1ALAww3DKMLmXo9n+jQlJDQCszDDMV14MPwIAGiXDg+FHP05qrH4EAFgGlRoAWJnTKdl4nhoAwAoCbPiRpAYAFmY4nTJMVmqsfgQANE4BVqmxUAQAYBlUagBgZU5DsgVOpUZSAwArMwyZfvQMSQ0A0BgZTkOGyUrNIKkBABolw4OHhLL6EQDQGAVapcbqRwCAZfh1pXb63yZOqdr0bRiAGaUn/Hc4Bv6htKz2b8zbVdIpo9L0cOIpVXs1hobk10ntxIkTkqStetfHkcDqml/q6wgQKE6cOCGHw1Hn9wkNDVVcXJy2Fnr2+xgXF6fQ0NA6x9HQbIYfD546nU4dOXJEkZGRstlsvg7HL5SWlioxMVEFBQWKiorydTiwKP7OzDMMQydOnFBCQoKCgrwzM1RRUaGqqiqPrg0NDVVYWJhX4mhIfl2pBQUFqU2bNr4Owy9FRUXxY4N6x9+ZOd6o0H4uLCzMLxNTXbBQBABgGSQ1AIBlkNQCjN1u16xZs2S3230dCiyMvzP4il8vFAEA4Oeo1AAAlkFSAwBYBkkNAGAZJDUAgGWQ1AJMTk6OLr74YoWFhal379768MMPfR0SLGTz5s0aOHCgEhISZLPZtGbNGl+HhABDUgsgr7/+ujIzMzVr1ix99NFH6tq1q9LT03Xs2DFfhwaLKC8vV9euXZWTk+PrUBCgWNIfQHr37q1evXrp+eefl1S7d2ZiYqLGjRunadOm+Tg6WI3NZtPbb7+twYMH+zoUBBAqtQBRVVWlXbt2KS0tzdUWFBSktLQ0bd++3YeRAYD3kNQCxLfffquamhrFxsa6tcfGxqqwsNBHUQGAd5HUAACWQVILEC1btlRwcLCKiorc2ouKihQXF+ejqADAu0hqASI0NFQ9evRQbm6uq83pdCo3N1epqak+jAwAvMevHxIKczIzM5WRkaGePXvqqquu0sKFC1VeXq4RI0b4OjRYRFlZmfbt2+d6nZ+fr927dysmJkZJSUk+jAyBgiX9Aeb555/Xk08+qcLCQnXr1k3Z2dnq3bu3r8OCRWzcuFH9+/c/oz0jI0PLly9v+IAQcEhqAADLYE4NAGAZJDUAgGWQ1AAAlkFSAwBYBkkNAGAZJDUAgGWQ1AAAlkFSgyUMHz7c7bld/fr108SJExs8jo0bN8pms+n48ePn7GP2idCPPPKIunXrVqe4vvrqK9lsNu3evbtO7wM0diQ11Jvhw4fLZrPJZrMpNDRUKSkpmjNnjk6dOlXvn/3WW2/p0UcfvaC+F5KIAPgH9n5Evbrpppu0bNkyVVZW6t1339WYMWMUEhKi6dOnn9G3qqpKoaGhXvncmJgYr7wPAP9CpYZ6ZbfbFRcXp7Zt22r06NFKS0vTO++8I+mnIcO5c+cqISFBHTp0kCQVFBTojjvuUHR0tGJiYjRo0CB99dVXrvesqalRZmamoqOj1aJFC02dOlW/3O3tl8OPlZWVevDBB5WYmCi73a6UlBQtWbJEX331lWuvwubNm8tms2n48OGSap9ikJWVpeTkZIWHh6tr165688033T7n3Xff1aWXXqrw8HD179/fLc4L9eCDD+rSSy9V06ZN1a5dO82YMUPV1dVn9HvxxReVmJiopk2b6o477lBJSYnb+VdeeUWdOnVSWFiYOnbsqBdeeMF0LIC/I6mhQYWHh6uqqsr1Ojc3V3l5edqwYYPWrVun6upqpaenKzIyUlu2bNH//M//qFmzZrrppptc1z399NNavny5li5dqq1bt6q4uFhvv/32eT/37rvv1l/+8hdlZ2drz549evHFF9WsWTMlJibqr3/9qyQpLy9PR48e1bPPPitJysrK0ooVK7R48WL95z//0aRJk3TXXXdp06ZNkmqT72233aaBAwdq9+7duvfeezVt2jTT/0wiIyO1fPlyff7553r22Wf18ssva8GCBW599u3bp9WrV2vt2rVav369Pv74Yz3wwAOu8ytXrtTMmTM1d+5c7dmzR/PmzdOMGTP06quvmo4H8GsGUE8yMjKMQYMGGYZhGE6n09iwYYNht9uNyZMnu87HxsYalZWVrmv+/Oc/Gx06dDCcTqerrbKy0ggPDzfee+89wzAMIz4+3pg/f77rfHV1tdGmTRvXZxmGYVx33XXGhAkTDMMwjLy8PEOSsWHDhrPG+a9//cuQZHz//feutoqKCqNp06bGtm3b3PqOHDnS+MMf/mAYhmFMnz7d6Ny5s9v5Bx988Iz3+iVJxttvv33O808++aTRo0cP1+tZs2YZwcHBxqFDh1xt//jHP4ygoCDj6NGjhmEYxiWXXGKsWrXK7X0effRRIzU11TAMw8jPzzckGR9//PE5PxewAubUUK/WrVunZs2aqbq6Wk6nU3/84x/1yCOPuM536dLFbR7tk08+0b59+xQZGen2PhUVFdq/f79KSkp09OhRt8flNGnSRD179jxjCPK03bt3Kzg4WNddd90Fx71v3z6dPHlSN9xwg1t7VVWVunfvLknas2fPGY/t8eSBq6+//rqys7O1f/9+lZWV6dSpU4qKinLrk5SUpIsuusjtc5xOp/Ly8hQZGan9+/dr5MiRGjVqlKvPqVOn5HA4TMcD+DOSGupV//79tWjRIoWGhiohIUFNmrj/yUVERLi9LisrU48ePbRy5coz3qtVq1YexRAeHm76mrKyMknS3//+d7dkItXOE3rL9u3bNWzYMM2ePVvp6elyOBx67bXX9PTTT5uO9eWXXz4jyQYHB3stVsAfkNRQryIiIpSSknLB/a+88kq9/vrrat269RnVymnx8fHasWOHrr32Wkm1FcmuXbt05ZVXnrV/ly5d5HQ6tWnTJqWlpZ1x/nSlWFNT42rr3Lmz7Ha7Dh48eM4Kr1OnTq5FL6d98MEHv/4lf2bbtm1q27atHnroIVfb119/fUa/gwcP6siRI0pISHB9TlBQkDp06KDY2FglJCTowIEDGjZsmKnPB6yGhSJoVIYNG6aWLVtq0KBB2rJli/Lz87Vx40aNHz9ehw4dkiRNmDBBjz/+uNasWaMvvvhCDzzwwHnvMbv44ouVkZGhe+65R2vWrHG95+rVqyVJbdu2lc1m07p16/TNN9+orKxMkZGRmjx5siZNmqRXX31V+/fv10cffaTnnnvOtfji/vvv1969ezVlyhTl5eVp1apVpp/u3L59ex08eFCvvfaa9u/fr+zs7LMuegkLC1NGRoY++eQTbdmyRePHj9cdd9yhuLg4SdLs2bOVlZWl7Oxsffnll/r000+1bNkyPfPMM6biAfyeryf1YF0/Xyhi5vzRo0eNu+++22jZsqVht9uNdu3aGaNGjTJKSkoMw6hdGDJhwgQjKirKiI6ONjIzM4277777nAtFDMMwfvjhB2PSpElGfHy8ERoaaqSkpBhLly51nZ8zZ44RFxdn2Gw2IyMjwzCM2sUtCxcuNDp06GCEhIQYrVq1MtLT041Nmza5rlu7dq2RkpJi2O12o2/fvsbSpUtNLxSZMmWK0aJFC6NZs2bGnXfeaSxYsMBwOByu87NmzTK6du1qvPDCC0ZCQoIRFhZm3H777UZxcbHb+65cudLo1q2bERoaajRv3ty49tprjbfeesswDBaKIHDYDOMcs+sAAPgZhh8BAJZBUgMAWAZJDQBgGSQ1AIBlkNQAAJZBUgMAWAZJDQBgGSQ1AIBlkNQAAJZBUgMAWAZJDQBgGSQ1AIBl/C8BfTVSRT1+XQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "disp = ConfusionMatrixDisplay(cm)\n",
    "disp.plot()\n",
    "disp.figure_.set_size_inches(w=5, h=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Boosting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Gradient Boosted Trees for Regression](imgs/gradientboosting.png)   \n",
    "For more information: https://www.geeksforgeeks.org/ml-gradient-boosting/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_dataset = load_iris()\n",
    "\n",
    "X = iris_dataset.data\n",
    "y = iris_dataset.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_gbm = GradientBoostingClassifier()\n",
    "\n",
    "iris_gbm.fit(X_train, y_train)\n",
    "y_pred = iris_gbm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        19\n",
      "           1       1.00      1.00      1.00        13\n",
      "           2       1.00      1.00      1.00        13\n",
      "\n",
      "    accuracy                           1.00        45\n",
      "   macro avg       1.00      1.00      1.00        45\n",
      "weighted avg       1.00      1.00      1.00        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(iris_gbm.score(X_test, y_test))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "disp = ConfusionMatrixDisplay(cm)\n",
    "disp.plot()\n",
    "disp.figure_.set_size_inches(w=5, h=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use Diagnostic Wisconsin Breast Cancer dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer_dataset = load_breast_cancer()\n",
    "\n",
    "X = cancer_dataset.data\n",
    "y = cancer_dataset.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer_gbm = GradientBoostingClassifier()\n",
    "\n",
    "cancer_gbm.fit(X_train, y_train)\n",
    "y_pred = cancer_gbm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9590643274853801\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.94      0.94        63\n",
      "           1       0.96      0.97      0.97       108\n",
      "\n",
      "    accuracy                           0.96       171\n",
      "   macro avg       0.96      0.95      0.96       171\n",
      "weighted avg       0.96      0.96      0.96       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(cancer_gbm.score(X_test, y_test))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "disp = ConfusionMatrixDisplay(cm)\n",
    "disp.plot()\n",
    "disp.figure_.set_size_inches(w=5, h=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Random Forests steps](imgs/random-forest-steps.PNG)   \n",
    "For more information: https://youtu.be/v6VJ2RO66Ag?si=EXjOA0eT-NzD9EH7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_dataset = load_iris()\n",
    "\n",
    "X = iris_dataset.data\n",
    "y = iris_dataset.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_forest = RandomForestClassifier(criterion=\"entropy\")\n",
    "\n",
    "iris_forest.fit(X_train, y_train)\n",
    "y_pred = iris_forest.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        19\n",
      "           1       1.00      1.00      1.00        13\n",
      "           2       1.00      1.00      1.00        13\n",
      "\n",
      "    accuracy                           1.00        45\n",
      "   macro avg       1.00      1.00      1.00        45\n",
      "weighted avg       1.00      1.00      1.00        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(iris_forest.score(X_test, y_test))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "disp = ConfusionMatrixDisplay(cm)\n",
    "disp.plot()\n",
    "disp.figure_.set_size_inches(w=5, h=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use Diagnostic Wisconsin Breast Cancer dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer_dataset = load_breast_cancer()\n",
    "\n",
    "X = cancer_dataset.data\n",
    "y = cancer_dataset.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer_forest = RandomForestClassifier(criterion=\"entropy\")\n",
    "\n",
    "cancer_forest.fit(X_train, y_train)\n",
    "y_pred = cancer_forest.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9707602339181286\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.94      0.96        63\n",
      "           1       0.96      0.99      0.98       108\n",
      "\n",
      "    accuracy                           0.97       171\n",
      "   macro avg       0.97      0.96      0.97       171\n",
      "weighted avg       0.97      0.97      0.97       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(cancer_forest.score(X_test, y_test))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "disp = ConfusionMatrixDisplay(cm)\n",
    "disp.plot()\n",
    "disp.figure_.set_size_inches(w=5, h=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}