Random Forest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Implementation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "breast_cancer = datasets.load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.DataFrame(data=breast_cancer.data,columns=breast_cancer.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>0.2419</td>\n",
       "      <td>0.07871</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>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>0.1812</td>\n",
       "      <td>0.05667</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>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>0.2069</td>\n",
       "      <td>0.05999</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>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>0.2597</td>\n",
       "      <td>0.09744</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>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>0.1809</td>\n",
       "      <td>0.05883</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 × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
       "0                 0.07871  ...         25.38          17.33           184.60   \n",
       "1                 0.05667  ...         24.99          23.41           158.80   \n",
       "2                 0.05999  ...         23.57          25.53           152.50   \n",
       "3                 0.09744  ...         14.91          26.50            98.87   \n",
       "4                 0.05883  ...         22.54          16.67           152.20   \n",
       "\n",
       "   worst area  worst smoothness  worst compactness  worst concavity  \\\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",
       "   worst concave points  worst symmetry  worst fractal dimension  \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 30 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>malignant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Target\n",
       "0  malignant\n",
       "1     benign"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_label = pd.DataFrame(data=breast_cancer.target_names,columns=['Target'])\n",
    "y_label.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = pd.DataFrame(data=breast_cancer.target,columns=['Target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   Target\n",
       "0       0\n",
       "1       0\n",
       "2       0\n",
       "3       0\n",
       "4       0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean radius                0\n",
       "mean texture               0\n",
       "mean perimeter             0\n",
       "mean area                  0\n",
       "mean smoothness            0\n",
       "mean compactness           0\n",
       "mean concavity             0\n",
       "mean concave points        0\n",
       "mean symmetry              0\n",
       "mean fractal dimension     0\n",
       "radius error               0\n",
       "texture error              0\n",
       "perimeter error            0\n",
       "area error                 0\n",
       "smoothness error           0\n",
       "compactness error          0\n",
       "concavity error            0\n",
       "concave points error       0\n",
       "symmetry error             0\n",
       "fractal dimension error    0\n",
       "worst radius               0\n",
       "worst texture              0\n",
       "worst perimeter            0\n",
       "worst area                 0\n",
       "worst smoothness           0\n",
       "worst compactness          0\n",
       "worst concavity            0\n",
       "worst concave points       0\n",
       "worst symmetry             0\n",
       "worst fractal dimension    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make Prediction on Test Set  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the performance of the Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve,roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prob = rf.predict_proba(X_test)\n",
    "y_prob_benign = y_prob[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr,tpr,threshold = roc_curve(y_test,y_prob_benign)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9975014697236919"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test,y_prob_benign)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x8b3e82ffc8>]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fpr,tpr,'bo--', linewidth=2, markersize=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_estimators = [1,2,4,8,16,32,64,100,200]\n",
    "train_results = []\n",
    "test_results = []\n",
    "\n",
    "for estimator in n_estimators:\n",
    "    rf = RandomForestClassifier(n_estimators=estimator,n_jobs=1)\n",
    "    rf.fit(X_train,y_train)\n",
    "    train_results.append(roc_auc_score(y_train,rf.predict_proba(X_train)[:,1]))\n",
    "    test_results.append(roc_auc_score(y_test,rf.predict_proba(X_test)[:,1]))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.legend_handler import HandlerLine2D\n",
    "line1, = plt.plot(n_estimators,train_results,\"b\",label=\"Train_AUC\")\n",
    "line2, = plt.plot(n_estimators,test_results,\"r\",label=\"Test_AUC\")\n",
    "plt.legend(handler_map={line1:HandlerLine2D(numpoints=2)})\n",
    "plt.ylabel(\"AUC Score\")\n",
    "plt.xlabel(\"n_estimators\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like at n_estimators=35 the test accuracy is very good along with training accuracy. So Low variance and low bias. We can stick to this value and use it in our final model.<br/><br/>\n",
    "Similarly we can also check for optimum value of 'max_depth','min_samples_split','min_samples_leaf','max_features' and 'max_leaf_nodes'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}