Random-Forest_R-KF.ipynb

{
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numara</th>\n",
       "      <th>yigin_kalinligi</th>\n",
       "      <th>hucre_boyutu_esbicim</th>\n",
       "      <th>hucre_sekil_esbicim</th>\n",
       "      <th>marjinal_yapisma</th>\n",
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      "text/plain": [
       "    numara  yigin_kalinligi  hucre_boyutu_esbicim  hucre_sekil_esbicim  \\\n",
       "0  1000025                5                     1                    1   \n",
       "1  1002945                5                     4                    4   \n",
       "2  1015425                3                     1                    1   \n",
       "3  1016277                6                     8                    8   \n",
       "4  1017023                4                     1                    1   \n",
       "\n",
       "   marjinal_yapisma  tek_epitel_hucre_boyut  ciplak_cekirdekler  \\\n",
       "0                 1                       2                   1   \n",
       "1                 5                       7                  10   \n",
       "2                 1                       2                   2   \n",
       "3                 1                       3                   4   \n",
       "4                 3                       2                   1   \n",
       "\n",
       "   yumusak_kromatin  normal_nukleoli  mitoz  sinif  \n",
       "0                 3                1      1      0  \n",
       "1                 3                2      1      0  \n",
       "2                 3                1      1      0  \n",
       "3                 3                7      1      0  \n",
       "4                 3                1      1      0  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "import pandas as pd \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "dataset=pd.read_csv(\"breastCancer_processed.csv\")\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = dataset.drop([\"sinif\",\"numara\"],axis=1).values\n",
    "y = dataset[\"sinif\"].values\n",
    "\n",
    "classifier= RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99        90\n",
      "           1       0.96      1.00      0.98        47\n",
      "\n",
      "    accuracy                           0.99       137\n",
      "   macro avg       0.98      0.99      0.98       137\n",
      "weighted avg       0.99      0.99      0.99       137\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.94      0.96        90\n",
      "           1       0.90      0.94      0.92        47\n",
      "\n",
      "    accuracy                           0.94       137\n",
      "   macro avg       0.93      0.94      0.94       137\n",
      "weighted avg       0.94      0.94      0.94       137\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        91\n",
      "           1       1.00      1.00      1.00        46\n",
      "\n",
      "    accuracy                           1.00       137\n",
      "   macro avg       1.00      1.00      1.00       137\n",
      "weighted avg       1.00      1.00      1.00       137\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.95      0.97        88\n",
      "           1       0.92      0.96      0.94        48\n",
      "\n",
      "    accuracy                           0.96       136\n",
      "   macro avg       0.95      0.96      0.95       136\n",
      "weighted avg       0.96      0.96      0.96       136\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.99      0.98        85\n",
      "           1       0.98      0.94      0.96        51\n",
      "\n",
      "    accuracy                           0.97       136\n",
      "   macro avg       0.97      0.96      0.97       136\n",
      "weighted avg       0.97      0.97      0.97       136\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.98      0.98        88\n",
      "           1       0.96      0.96      0.96        49\n",
      "\n",
      "    accuracy                           0.97       137\n",
      "   macro avg       0.97      0.97      0.97       137\n",
      "weighted avg       0.97      0.97      0.97       137\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.95      0.97        93\n",
      "           1       0.90      0.98      0.93        44\n",
      "\n",
      "    accuracy                           0.96       137\n",
      "   macro avg       0.94      0.96      0.95       137\n",
      "weighted avg       0.96      0.96      0.96       137\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.98        91\n",
      "           1       0.96      0.98      0.97        46\n",
      "\n",
      "    accuracy                           0.98       137\n",
      "   macro avg       0.97      0.98      0.98       137\n",
      "weighted avg       0.98      0.98      0.98       137\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.99      0.98        87\n",
      "           1       0.98      0.96      0.97        49\n",
      "\n",
      "    accuracy                           0.98       136\n",
      "   macro avg       0.98      0.97      0.98       136\n",
      "weighted avg       0.98      0.98      0.98       136\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.96      0.98        85\n",
      "           1       0.94      0.98      0.96        51\n",
      "\n",
      "    accuracy                           0.97       136\n",
      "   macro avg       0.97      0.97      0.97       136\n",
      "weighted avg       0.97      0.97      0.97       136\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.96      0.97        89\n",
      "           1       0.92      0.96      0.94        48\n",
      "\n",
      "    accuracy                           0.96       137\n",
      "   macro avg       0.95      0.96      0.95       137\n",
      "weighted avg       0.96      0.96      0.96       137\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.99      0.98        89\n",
      "           1       0.98      0.96      0.97        48\n",
      "\n",
      "    accuracy                           0.98       137\n",
      "   macro avg       0.98      0.97      0.98       137\n",
      "weighted avg       0.98      0.98      0.98       137\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.97      0.98        92\n",
      "           1       0.94      0.98      0.96        45\n",
      "\n",
      "    accuracy                           0.97       137\n",
      "   macro avg       0.96      0.97      0.97       137\n",
      "weighted avg       0.97      0.97      0.97       137\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.97      0.98        90\n",
      "           1       0.94      0.98      0.96        46\n",
      "\n",
      "    accuracy                           0.97       136\n",
      "   macro avg       0.96      0.97      0.97       136\n",
      "weighted avg       0.97      0.97      0.97       136\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99        84\n",
      "           1       1.00      0.98      0.99        52\n",
      "\n",
      "    accuracy                           0.99       136\n",
      "   macro avg       0.99      0.99      0.99       136\n",
      "weighted avg       0.99      0.99      0.99       136\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.96      0.98        80\n",
      "           1       0.95      1.00      0.97        57\n",
      "\n",
      "    accuracy                           0.98       137\n",
      "   macro avg       0.97      0.98      0.98       137\n",
      "weighted avg       0.98      0.98      0.98       137\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.95      0.97        93\n",
      "           1       0.90      0.98      0.93        44\n",
      "\n",
      "    accuracy                           0.96       137\n",
      "   macro avg       0.94      0.96      0.95       137\n",
      "weighted avg       0.96      0.96      0.96       137\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99        98\n",
      "           1       1.00      0.97      0.99        39\n",
      "\n",
      "    accuracy                           0.99       137\n",
      "   macro avg       0.99      0.99      0.99       137\n",
      "weighted avg       0.99      0.99      0.99       137\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.96      0.96        80\n",
      "           1       0.95      0.95      0.95        56\n",
      "\n",
      "    accuracy                           0.96       136\n",
      "   macro avg       0.95      0.95      0.95       136\n",
      "weighted avg       0.96      0.96      0.96       136\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.98      0.97        93\n",
      "           1       0.95      0.93      0.94        43\n",
      "\n",
      "    accuracy                           0.96       136\n",
      "   macro avg       0.96      0.95      0.96       136\n",
      "weighted avg       0.96      0.96      0.96       136\n",
      "\n",
      "Ortalama dogruluk: 0.9710793258909407\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RepeatedKFold\n",
    "\n",
    "rkf = RepeatedKFold(n_splits=5, n_repeats=4, random_state=5)\n",
    "k_fold_scores=[]\n",
    "\n",
    "sc = StandardScaler()\n",
    "\n",
    "for train_index, test_index in rkf.split(x):\n",
    "    X_train, X_test = x[train_index], x[test_index]\n",
    "    y_train, y_test = y[train_index], y[test_index]\n",
    "\n",
    "    X_train = sc.fit_transform(X_train)\n",
    "    X_test = sc.transform(X_test)\n",
    "\n",
    "    classifier.fit(X_train, y_train)\n",
    "\n",
    "    accuracy = classifier.score(X_test, y_test)\n",
    "    y_pred = classifier.predict(X_test)\n",
    "    cm_nb = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "    print(classification_report(y_test,y_pred))\n",
    "\n",
    "    k_fold_scores.append(accuracy)\n",
    "\n",
    "print(\"Ortalama dogruluk:\", sum(k_fold_scores) / len(k_fold_scores))"
   ]
  }
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