8 4 6 RANDOM FORESTS.ipynb

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 "cells": [
  {
   "source": [
    "# RANDOM FORESTS"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import statsmodels.api as sm \n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn.preprocessing import scale, StandardScaler\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, mean_squared_error, r2_score, roc_auc_score, roc_curve, classification_report "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"verisetleri\\diabetes.csv\")\n",
    "y = df[\"Outcome\"]\n",
    "X = df.drop([\"Outcome\"], axis=1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MODEL & TAHMİN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_model = RandomForestClassifier().fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': 'auto',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_impurity_split': None,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': None,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "rf_model.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Birinci derecede önemli parametreler\n",
    "# Kaç tane ağaç olacak? n_estimators\n",
    "# Bölünmelerde göz önünde bulundurulacak değişken sayısı, max_features\n",
    "# İkinci düzeyde önemli parametreler max_depth, min_samples_leaf, min_samples_split\n",
    "# min_samples_split : Ağacın ne kadar dallanacağını kontrol eder. Yerine max_depth te kullanılabilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = rf_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.7575757575757576"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MODEL TUNNING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_params = {\"n_estimators\":[100, 200, 500, 1000], \"max_features\":[3,5,7,8], \"min_samples_split\":[2,5,10,20] }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Fitting 10 folds for each of 64 candidates, totalling 640 fits\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:   14.2s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   45.3s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  1.7min\n",
      "[Parallel(n_jobs=-1)]: Done 640 out of 640 | elapsed:  3.4min finished\n"
     ]
    }
   ],
   "source": [
    "rf_cv_model = GridSearchCV(rf, rf_params, cv=10, n_jobs=-1, verbose=2).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'max_features': 5, 'min_samples_split': 5, 'n_estimators': 200}"
      ]
     },
     "metadata": {},
     "execution_count": 21
    }
   ],
   "source": [
    "rf_cv_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_final_model_tuned = RandomForestClassifier(max_features=5,min_samples_split=5,n_estimators=200).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = rf_final_model_tuned.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.7489177489177489"
      ]
     },
     "metadata": {},
     "execution_count": 40
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "source": [
    "### DEĞİŞKEN ÖNEM DÜZEYLERİ - ( ileri seviye )"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': 5,\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_impurity_split': None,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 5,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 200,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': None,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "metadata": {},
     "execution_count": 43
    }
   ],
   "source": [
    "rf_final_model_tuned.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([0.05387523, 0.34713193, 0.07234975, 0.0534474 , 0.05144549,\n",
       "       0.16923228, 0.10789882, 0.1446191 ])"
      ]
     },
     "metadata": {},
     "execution_count": 44
    }
   ],
   "source": [
    "rf_final_model_tuned.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "feature_imp = pd.Series(rf_final_model_tuned.feature_importances_, index=X_train.columns).sort_values(ascending=False)\n",
    "\n",
    "sns.barplot(x=feature_imp, y=feature_imp.index)\n",
    "plt.xlabel('Değişken Önem Skorları')\n",
    "plt.ylabel('Değişkenler')\n",
    "plt.title(\"Değişken Önem Düzeyleri\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}