{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "208b46aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dependencies and packages neededfrom sklearn import datasets\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
    "from sklearn import metrics\n",
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2d7471eb",
   "metadata": {},
   "outputs": [
    {
     "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_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}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this step is just to demonstrate the adjustable parameters within the algorithm and not a necessary step\n",
    "# Specifically look at n_estimators (how many individual trees to build) and max_depth (how deep each tree will go)\n",
    "RandomForestClassifier().get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "616152d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'criterion': 'squared_error',\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_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}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this step is just to demonstrate the adjustable parameters within the algorithm and not a necessary step\n",
    "RandomForestRegressor().get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c1a1f233",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The length of the dataset is  768\n",
      "The shape of the dataset (Number of columns / features) is  (768, 10)\n"
     ]
    }
   ],
   "source": [
    "# load the dataset (stored in a variable)\n",
    "diabetes = pd.read_csv('./diabetes.csv', sep=',',header = 0)\n",
    "\n",
    "print (\"The length of the dataset is \", len(diabetes))\n",
    "print(\"The shape of the dataset (Number of columns / features) is \", diabetes.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2bfbb63d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PatientId  Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin  \\\n",
       "0          1            6      148             72             35        0   \n",
       "1          2            1       85             66             29        0   \n",
       "2          3            8      183             64              0        0   \n",
       "3          4            1       89             66             23       94   \n",
       "4          5            0      137             40             35      168   \n",
       "\n",
       "    BMI  DiabetesPedigreeFunction  Age  Outcome  \n",
       "0  33.6                     0.627   50        1  \n",
       "1  26.6                     0.351   31        0  \n",
       "2  23.3                     0.672   32        1  \n",
       "3  28.1                     0.167   21        0  \n",
       "4  43.1                     2.288   33        1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7dc6c9bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 576 entries, 357 to 102\n",
      "Data columns (total 10 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   PatientId                 576 non-null    int64  \n",
      " 1   Pregnancies               576 non-null    int64  \n",
      " 2   Glucose                   576 non-null    int64  \n",
      " 3   BloodPressure             576 non-null    int64  \n",
      " 4   SkinThickness             576 non-null    int64  \n",
      " 5   Insulin                   576 non-null    int64  \n",
      " 6   BMI                       576 non-null    float64\n",
      " 7   DiabetesPedigreeFunction  576 non-null    float64\n",
      " 8   Age                       576 non-null    int64  \n",
      " 9   Outcome                   576 non-null    int64  \n",
      "dtypes: float64(2), int64(8)\n",
      "memory usage: 49.5 KB\n"
     ]
    }
   ],
   "source": [
    "# split the data into train and test using the SciKitLearn package\n",
    "train,test=train_test_split(diabetes,test_size=0.25,random_state=42)\n",
    "# view the information\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3c9cf104",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 192 entries, 668 to 615\n",
      "Data columns (total 8 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Pregnancies               192 non-null    int64  \n",
      " 1   Glucose                   192 non-null    int64  \n",
      " 2   BloodPressure             192 non-null    int64  \n",
      " 3   SkinThickness             192 non-null    int64  \n",
      " 4   Insulin                   192 non-null    int64  \n",
      " 5   BMI                       192 non-null    float64\n",
      " 6   DiabetesPedigreeFunction  192 non-null    float64\n",
      " 7   Age                       192 non-null    int64  \n",
      "dtypes: float64(2), int64(6)\n",
      "memory usage: 13.5 KB\n"
     ]
    }
   ],
   "source": [
    "# Ensure only labels are saved in y variables\n",
    "y_train=train['Outcome']\n",
    "y_test=test['Outcome']\n",
    "#drop the unwanted / irrelevant columns, features are stored in X variables\n",
    "X_train=train.drop(['PatientId','Outcome'],axis=1)\n",
    "X_test=test.drop(['PatientId','Outcome'],axis=1)\n",
    "# view the information\n",
    "X_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "49bb568e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Pregnancies',\n",
       " 'Glucose',\n",
       " 'BloodPressure',\n",
       " 'SkinThickness',\n",
       " 'Insulin',\n",
       " 'BMI',\n",
       " 'DiabetesPedigreeFunction',\n",
       " 'Age']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mtarget =  diabetes[\"Outcome\"].values \n",
    "mtarget = np.array(mtarget, dtype='int')  #array of class targets results win=1, lose=0\n",
    "values = np.array(diabetes.values,dtype='int') #array of attribute values for training data\n",
    "values = values[:,1:9] #slice-> cols 1,2,3 and 4 kept.  cols 0 and 9 excluded.\n",
    "targetNames=['Diabetes','No Diabetes']\n",
    "feature_names=list(diabetes)\n",
    "\n",
    "\n",
    "feature_names=feature_names[1:9]   #slice-> cols 1,2,3 and 4 kept.  cols 0 and 5 excluded.\n",
    "feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9e4b22f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=5, n_estimators=50, random_state=42)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_clf = RandomForestClassifier(n_estimators = 50, random_state = 42, max_depth = 5)\n",
    "\n",
    "rf_clf.fit(values, mtarget)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c7975716",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 1 0 0 0 1 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 1 1 1 1 0 1 1\n",
      " 0 0 1 0 0 0 0 0 1 1 0 0 1 0 1 1 0 0 0 1 0 0 1 1 0 0 1 0 0 0 1 0 1 1 0 0 0\n",
      " 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 1 0 1 0 0 1 0 1 0 1 1 1 0 0 1 0 1 0\n",
      " 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0\n",
      " 0 1 0 0 1 0 0 1 0 0 1 0 1 1 0 0 0 1 0 0 1 1 1 0 0 0 0 0 0 0 1 1 0 0 0 1 1\n",
      " 0 0 0 0 0 0 0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\base.py:443: UserWarning: X has feature names, but RandomForestClassifier was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "192"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_rf = rf_clf.predict(X_test)\n",
    "print(y_pred_rf)\n",
    "len(y_pred_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ea92bc77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_clf_def = RandomForestClassifier()\n",
    "\n",
    "rf_clf_def.fit(values, mtarget)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "82c201e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 1 1 0 0 0 0 0 1 0 0 1 0 1 1 1 1 0 1 1\n",
      " 1 0 1 0 0 0 1 0 1 1 0 0 0 0 1 1 1 0 0 0 0 0 1 1 0 0 1 0 0 0 1 0 1 0 0 0 1\n",
      " 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 0 1 1 1 0\n",
      " 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 1 1 1 0 1 1 0 1 1 0 1 1 1 0 0 0 0 0 0 0 1 0\n",
      " 0 1 0 0 1 0 0 1 0 0 1 0 1 1 0 0 0 1 0 0 0 1 0 1 1 0 0 0 0 0 1 1 0 0 0 1 1\n",
      " 0 0 0 1 0 1 0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\base.py:443: UserWarning: X has feature names, but RandomForestClassifier was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "192"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_rf_def = rf_clf_def.predict(X_test)\n",
    "print(y_pred_rf_def)\n",
    "len(y_pred_rf_def)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "11c1dacd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "668    0\n",
      "324    0\n",
      "624    0\n",
      "690    0\n",
      "473    0\n",
      "      ..\n",
      "554    0\n",
      "319    1\n",
      "594    0\n",
      "6      1\n",
      "615    0\n",
      "Name: Outcome, Length: 192, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0d04eca3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[110  13]\n",
      " [ 20  49]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.846     0.894     0.870       123\n",
      "           1      0.790     0.710     0.748        69\n",
      "\n",
      "    accuracy                          0.828       192\n",
      "   macro avg      0.818     0.802     0.809       192\n",
      "weighted avg      0.826     0.828     0.826       192\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# display the metrics of the model's classification through confusion matrix and classification report\n",
    "# (accuracy / precision / recall / f1)\n",
    "print(metrics.confusion_matrix(y_test, y_pred_rf))\n",
    "\n",
    "print(metrics.classification_report(y_test, y_pred_rf, digits=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "881cf0b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[123   0]\n",
      " [  1  68]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.992     1.000     0.996       123\n",
      "           1      1.000     0.986     0.993        69\n",
      "\n",
      "    accuracy                          0.995       192\n",
      "   macro avg      0.996     0.993     0.994       192\n",
      "weighted avg      0.995     0.995     0.995       192\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.confusion_matrix(y_test, y_pred_rf_def))\n",
    "\n",
    "print(metrics.classification_report(y_test, y_pred_rf_def, digits=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c1fae681",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Actual  Predicted\n",
      "668       0          0\n",
      "324       0          0\n",
      "624       0          0\n",
      "690       0          0\n",
      "473       0          1\n",
      "..      ...        ...\n",
      "554       0          0\n",
      "319       1          0\n",
      "594       0          0\n",
      "6         1          0\n",
      "615       0          0\n",
      "\n",
      "[192 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "# use Pandas to create a dataframe to view the ground truth and classifications side by side\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0b961a57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.846     0.894     0.870       123\n",
      "           1      0.790     0.710     0.748        69\n",
      "\n",
      "    accuracy                          0.828       192\n",
      "   macro avg      0.818     0.802     0.809       192\n",
      "weighted avg      0.826     0.828     0.826       192\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Use Seaborn to create a confusion matrix heatmap and MatPlotLib to display\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "confusion_matrix = pd.crosstab(df['Actual'], df['Predicted'], rownames=['Actual'], colnames=['Predicted'])\n",
    "sn.he\n",
    "atmap(confusion_matrix, annot=True)\n",
    "plt.show()\n",
    "print(metrics.classification_report(y_test, y_pred_rf, digits=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "71b587ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_results(results):\n",
    "    print('The Best Parameters are: {}\\n'.format(results.best_params_))\n",
    "    means = results.cv_results_['mean_test_score']\n",
    "    stds = results.cv_results_['std_test_score']\n",
    "    \n",
    "    for mean, std, params in zip(means, stds, results.cv_results_['params']):\n",
    "        print('{} (=/-{}) for {}'.format(round(mean, 3), round(std * 2, 3), params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2d82b7f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Best Parameters are: {'max_depth': 16, 'n_estimators': 250}\n",
      "\n",
      "0.729 (=/-0.062) for {'max_depth': 2, 'n_estimators': 5}\n",
      "0.746 (=/-0.046) for {'max_depth': 2, 'n_estimators': 50}\n",
      "0.75 (=/-0.035) for {'max_depth': 2, 'n_estimators': 250}\n",
      "0.75 (=/-0.074) for {'max_depth': 4, 'n_estimators': 5}\n",
      "0.766 (=/-0.065) for {'max_depth': 4, 'n_estimators': 50}\n",
      "0.764 (=/-0.068) for {'max_depth': 4, 'n_estimators': 250}\n",
      "0.745 (=/-0.098) for {'max_depth': 8, 'n_estimators': 5}\n",
      "0.755 (=/-0.093) for {'max_depth': 8, 'n_estimators': 50}\n",
      "0.766 (=/-0.088) for {'max_depth': 8, 'n_estimators': 250}\n",
      "0.723 (=/-0.053) for {'max_depth': 16, 'n_estimators': 5}\n",
      "0.764 (=/-0.069) for {'max_depth': 16, 'n_estimators': 50}\n",
      "0.767 (=/-0.067) for {'max_depth': 16, 'n_estimators': 250}\n",
      "0.727 (=/-0.061) for {'max_depth': 32, 'n_estimators': 5}\n",
      "0.762 (=/-0.05) for {'max_depth': 32, 'n_estimators': 50}\n",
      "0.766 (=/-0.073) for {'max_depth': 32, 'n_estimators': 250}\n",
      "0.727 (=/-0.061) for {'max_depth': None, 'n_estimators': 5}\n",
      "0.762 (=/-0.05) for {'max_depth': None, 'n_estimators': 50}\n",
      "0.766 (=/-0.073) for {'max_depth': None, 'n_estimators': 250}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "parameters = {\n",
    "    'n_estimators' : [5, 50, 250],\n",
    "    'max_depth' : [2,4,8,16,32, None]\n",
    "}\n",
    "\n",
    "cv = GridSearchCV(rf_clf, parameters, cv = 5)\n",
    "cv.fit(values, mtarget)\n",
    "\n",
    "print_results(cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3d87e427",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import learning_curve\n",
    "train_sizes, train_scores, test_scores = learning_curve(cv, values, mtarget, cv=10, scoring='accuracy', n_jobs=-1, train_sizes=np.linspace(0.01, 1.0, 50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4296d1fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_mean = np.mean(train_scores, axis=1)\n",
    "train_std = np.std(train_scores, axis=1)\n",
    "\n",
    "test_mean = np.mean(test_scores, axis=1)\n",
    "test_std = np.std(test_scores, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c346d60f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(1, figsize=(10,10))\n",
    "plt.plot(train_sizes, train_mean, '--', color=\"#111111\",  label=\"Training score\")\n",
    "plt.plot(train_sizes, test_mean, color=\"#111111\", label=\"Cross-validation score\")\n",
    "\n",
    "plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, color=\"#DDDDDD\")\n",
    "plt.fill_between(train_sizes, test_mean - test_std, test_mean + test_std, color=\"#DDDDDD\")\n",
    "\n",
    "plt.title(\"Learning Curve\")\n",
    "plt.xlabel(\"Training Set Size\"), plt.ylabel(\"Accuracy Score\"), plt.legend(loc=\"best\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b74b0b5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import learning_curve\n",
    "train_sizes_rf, train_scores_rf, test_scores_rf = learning_curve(RandomForestClassifier(), values, mtarget, cv=10, scoring='accuracy', n_jobs=-1, train_sizes=np.linspace(0.01, 1.0, 50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "95e36f63",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_mean_rf = np.mean(train_scores_rf, axis=1)\n",
    "train_std_rf = np.std(train_scores, axis=1)\n",
    "\n",
    "test_mean_rf = np.mean(test_scores_rf, axis=1)\n",
    "test_std_rf = np.std(test_scores_rf, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "75c01cca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(1, figsize=(10,10))\n",
    "plt.plot(train_sizes_rf, train_mean_rf, '--', color=\"#111111\",  label=\"Training score\")\n",
    "plt.plot(train_sizes_rf, test_mean_rf, color=\"#111111\", label=\"Cross-validation score\")\n",
    "\n",
    "plt.fill_between(train_sizes_rf, train_mean_rf - train_std_rf, train_mean_rf + train_std_rf, color=\"#DDDDDD\")\n",
    "plt.fill_between(train_sizes_rf, test_mean_rf - test_std_rf, test_mean_rf + test_std_rf, color=\"#DDDDDD\")\n",
    "\n",
    "plt.title(\"Learning Curve\")\n",
    "plt.xlabel(\"Training Set Size\"), plt.ylabel(\"Accuracy Score\"), plt.legend(loc=\"best\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5fa05929",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}