Scikit-Learn Hyperparameter Optimization.ipynb

{
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  {
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   "source": [
    "# Standard imports\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Classifier Workflow for Classifying\n",
    "\n",
    "#### Get the data ready"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "      <th>age</th>\n",
       "      <th>sex</th>\n",
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       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
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       "      <th>slope</th>\n",
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       "      <td>1.4</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "heart_disease = pd.read_csv('data/heart-disease.csv')\n",
    "heart_disease.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <td>241</td>\n",
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       "      <td>264</td>\n",
       "      <td>0</td>\n",
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       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>131</td>\n",
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       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
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       "<p>303 rows × 14 columns</p>\n",
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      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "0        0   0     1       1  \n",
       "1        0   0     2       1  \n",
       "2        2   0     2       1  \n",
       "3        2   0     2       1  \n",
       "4        2   0     2       1  \n",
       "..     ...  ..   ...     ...  \n",
       "298      1   0     3       0  \n",
       "299      1   0     3       0  \n",
       "300      1   2     3       0  \n",
       "301      1   1     3       0  \n",
       "302      1   1     2       0  \n",
       "\n",
       "[303 rows x 14 columns]"
      ]
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     "execution_count": 3,
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     "output_type": "execute_result"
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   "source": [
    "heart_disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create X (all the feature columns)\n",
    "X = heart_disease.drop(\"target\", axis=1)\n",
    "\n",
    "# Create y (the target column)\n",
    "y = heart_disease[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
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       "      <td>163</td>\n",
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       "      <td>0.6</td>\n",
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      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  \n",
       "0   0     1  \n",
       "1   0     2  \n",
       "2   0     2  \n",
       "3   0     2  \n",
       "4   0     2  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0    1\n",
       " 1    1\n",
       " 2    1\n",
       " 3    1\n",
       " 4    1\n",
       " Name: target, dtype: int64,\n",
       " 1    165\n",
       " 0    138\n",
       " Name: target, dtype: int64)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head(), y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((227, 13), (76, 13), (227,), (76,))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split the data into training and test sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
    "\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We'll use a Random Forest\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "clf = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fit the model to the data and use it to make a prediction\n",
    "Fitting the model on the data involves passing it the data and asking it to figure out the patterns. \n",
    "\n",
    "If there are labels (supervised learning), the model tries to work out the relationship between the data and the labels. \n",
    "\n",
    "If there are no labels (unsupervised learning), the model tries to find patterns and group similar samples together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>417</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>132</td>\n",
       "      <td>341</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112</td>\n",
       "      <td>149</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>112</td>\n",
       "      <td>230</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>263</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>105</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "28    65    0   2       140   417    1        0      157      0      0.8   \n",
       "215   43    0   0       132   341    1        0      136      1      3.0   \n",
       "151   71    0   0       112   149    0        1      125      0      1.6   \n",
       "261   52    1   0       112   230    0        1      160      0      0.0   \n",
       "139   64    1   0       128   263    0        1      105      1      0.2   \n",
       "\n",
       "     slope  ca  thal  \n",
       "28       2   1     2  \n",
       "215      1   0     3  \n",
       "151      1   0     2  \n",
       "261      2   1     2  \n",
       "139      1   1     3  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1,\n",
       "       1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1,\n",
       "       0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0,\n",
       "       0, 0, 1, 1, 1, 0, 0, 0, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the model on the training set\n",
    "clf.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7631578947368421"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the model on the test set\n",
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.69      0.82      0.75        33\n",
      "           1       0.84      0.72      0.77        43\n",
      "\n",
      "    accuracy                           0.76        76\n",
      "   macro avg       0.77      0.77      0.76        76\n",
      "weighted avg       0.77      0.76      0.76        76\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "\n",
    "print(classification_report(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[27,  6],\n",
       "       [12, 31]], dtype=int64)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_mat = confusion_matrix(y_test, y_preds)\n",
    "conf_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7631578947368421"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment to improve hyperparameters (ONLY 1 FOR NOW)\n",
    "    \n",
    "Different models you use will have different hyperparameters you can tune. For the case of our model, the `RandomForestClassifier()`, we'll start trying different values for `n_estimators`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trying model with 10 estimators...\n",
      "Model accuracy on test set: 73.68421052631578%\n",
      "\n",
      "Trying model with 20 estimators...\n",
      "Model accuracy on test set: 71.05263157894737%\n",
      "\n",
      "Trying model with 30 estimators...\n",
      "Model accuracy on test set: 76.31578947368422%\n",
      "\n",
      "Trying model with 40 estimators...\n",
      "Model accuracy on test set: 75.0%\n",
      "\n",
      "Trying model with 50 estimators...\n",
      "Model accuracy on test set: 77.63157894736842%\n",
      "\n",
      "Trying model with 60 estimators...\n",
      "Model accuracy on test set: 78.94736842105263%\n",
      "\n",
      "Trying model with 70 estimators...\n",
      "Model accuracy on test set: 73.68421052631578%\n",
      "\n",
      "Trying model with 80 estimators...\n",
      "Model accuracy on test set: 73.68421052631578%\n",
      "\n",
      "Trying model with 90 estimators...\n",
      "Model accuracy on test set: 76.31578947368422%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Try different numbers of estimators (trees)... (no cross-validation)\n",
    "np.random.seed(42)\n",
    "for i in range(10, 100, 10):\n",
    "    print(f\"Trying model with {i} estimators...\")\n",
    "    model = RandomForestClassifier(n_estimators=i).fit(X_train, y_train)\n",
    "    print(f\"Model accuracy on test set: {model.score(X_test, y_test) * 100}%\")\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trying model with 10 estimators...\n",
      "Model accuracy on test set: 73.68421052631578%\n",
      "Cross-validation score: 78.53551912568305%\n",
      "\n",
      "Trying model with 20 estimators...\n",
      "Model accuracy on test set: 76.31578947368422%\n",
      "Cross-validation score: 79.84699453551912%\n",
      "\n",
      "Trying model with 30 estimators...\n",
      "Model accuracy on test set: 76.31578947368422%\n",
      "Cross-validation score: 80.50819672131148%\n",
      "\n",
      "Trying model with 40 estimators...\n",
      "Model accuracy on test set: 76.31578947368422%\n",
      "Cross-validation score: 82.15300546448088%\n",
      "\n",
      "Trying model with 50 estimators...\n",
      "Model accuracy on test set: 76.31578947368422%\n",
      "Cross-validation score: 81.1639344262295%\n",
      "\n",
      "Trying model with 60 estimators...\n",
      "Model accuracy on test set: 77.63157894736842%\n",
      "Cross-validation score: 83.47540983606557%\n",
      "\n",
      "Trying model with 70 estimators...\n",
      "Model accuracy on test set: 75.0%\n",
      "Cross-validation score: 81.83060109289617%\n",
      "\n",
      "Trying model with 80 estimators...\n",
      "Model accuracy on test set: 75.0%\n",
      "Cross-validation score: 82.81420765027322%\n",
      "\n",
      "Trying model with 90 estimators...\n",
      "Model accuracy on test set: 73.68421052631578%\n",
      "Cross-validation score: 82.81967213114754%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# With cross-validation\n",
    "np.random.seed(42)\n",
    "for i in range(10, 100, 10):\n",
    "    print(f\"Trying model with {i} estimators...\")\n",
    "    model = RandomForestClassifier(n_estimators=i).fit(X_train, y_train)\n",
    "    print(f\"Model accuracy on test set: {model.score(X_test, y_test) * 100}%\")\n",
    "    print(f\"Cross-validation score: {np.mean(cross_val_score(model, X, y, cv=5)) * 100}%\")\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter improvement with GridSearchCV (ONLY 1 HYPERPARAMETER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 80}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Another way to do it with GridSearchCV...\n",
    "np.random.seed(42)\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Define the parameters to search over\n",
    "\n",
    "param_grid = {'n_estimators': [i for i in range(10, 100, 10)]}\n",
    "\n",
    "# Setup the grid search\n",
    "grid = GridSearchCV(RandomForestClassifier(),\n",
    "                    param_grid,\n",
    "                    cv=5)\n",
    "\n",
    "# Fit the grid search to the data\n",
    "grid.fit(X, y)\n",
    "\n",
    "# Find the best parameters\n",
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=80)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the model to be the best estimator\n",
    "clf = grid.best_estimator_\n",
    "clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the best model\n",
    "clf = clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7631578947368421"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the best model scores\n",
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save a model for someone else to use\n",
    "\n",
    "You can save a scikit-learn model using Python's in-built `pickle` module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# Save an existing model to file\n",
    "pickle.dump(model, open(\"random_forest_model_1.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7368421052631579"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load a saved model and make a prediction\n",
    "loaded_model = pickle.load(open(\"random_forest_model_1.pkl\", \"rb\"))\n",
    "loaded_model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification model evaluation metrics\n",
    "\n",
    "Four of the main evaluation metrics/methods you'll come across for classification models are:\n",
    "\n",
    "1. Accuracy\n",
    "2. Area under ROC curve\n",
    "3. Confusion matrix\n",
    "4. Classification report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* True positive = model predicts 1 when truth is 1\n",
    "* False positive = model predicts 1 when truth is 0\n",
    "* True negative = model predicts 0 when truth is 0\n",
    "* False negative = model predicts 0 when truth is 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Heart Disease Classifier Accuracy: 76.32%\n"
     ]
    }
   ],
   "source": [
    "# Accuracy as percentage\n",
    "print(f\"Heart Disease Classifier Accuracy: {clf.score(X_test, y_test) * 100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AUC/ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.        , 0.        , 0.        , 0.03030303,\n",
       "       0.03030303, 0.03030303, 0.03030303, 0.03030303, 0.03030303,\n",
       "       0.06060606, 0.06060606, 0.06060606, 0.06060606, 0.09090909,\n",
       "       0.09090909, 0.09090909, 0.12121212, 0.12121212, 0.15151515,\n",
       "       0.15151515, 0.18181818, 0.18181818, 0.21212121, 0.21212121,\n",
       "       0.24242424, 0.24242424, 0.3030303 , 0.3030303 , 0.33333333,\n",
       "       0.33333333, 0.42424242, 0.48484848, 0.54545455, 0.63636364,\n",
       "       0.72727273, 0.84848485, 0.87878788, 1.        ])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "# Make predictions with probabilities\n",
    "y_probs = clf.predict_proba(X_test)\n",
    "\n",
    "# Keep the probabilites of the positive class only\n",
    "y_probs = y_probs[:, 1]\n",
    "\n",
    "# Calculate fpr, tpr and thresholds\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_probs)\n",
    "\n",
    "# Check the false positive rate\n",
    "fpr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_roc_curve(fpr, tpr):\n",
    "    \"\"\"\n",
    "    Plots a ROC curve given the false positve rate (fpr) and \n",
    "    true postive rate (tpr) of a classifier.\n",
    "    \"\"\"\n",
    "    # Plot ROC curve\n",
    "    plt.plot(fpr, tpr, color='orange', label='ROC')\n",
    "    \n",
    "    plt.plot([0, 1], [0, 1], color='darkblue', linestyle='--', label='Guessing')\n",
    "    # Customize the plot\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "plot_roc_curve(fpr, tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8731501057082452"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "roc_auc_score(y_test, y_probs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[27,  6],\n",
       "       [12, 31]], dtype=int64)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "y_preds = clf.predict(X_test)\n",
    "\n",
    "cm = confusion_matrix(y_test, y_preds)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Predicted Label</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual Label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted Label   0   1\n",
       "Actual Label           \n",
       "0                27   6\n",
       "1                12  31"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(y_test, \n",
    "            y_preds, \n",
    "            rownames=[\"Actual Label\"], \n",
    "            colnames=[\"Predicted Label\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.24.2'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn\n",
    "sklearn.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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"
    }
   ],
   "source": [
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
    "                              display_labels=clf.classes_)\n",
    "disp.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.classes_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Precision** - Indicates the proportion of positive identifications (model predicted class `1`) which were actually correct. A model which produces no false positives has a precision of 1.0.\n",
    "* **Recall** - Indicates the proportion of actual positives which were correctly classified. A model which produces no false negatives has a recall of 1.0.\n",
    "* **F1 score** - A combination of precision and recall. A perfect model achieves an F1 score of 1.0.\n",
    "* **Support** - The number of samples each metric was calculated on.\n",
    "* **Accuracy** - The accuracy of the model in decimal form. Perfect accuracy is equal to 1.0, in other words, getting the prediction right 100% of the time.\n",
    "* **Macro avg** - Short for macro average, the average precision, recall and F1 score between classes. Macro avg doesn't take class imbalance into effect. So if you do have class imbalances (more examples of one class than another), you should pay attention to this.\n",
    "* **Weighted avg** - Short for weighted average, the weighted average precision, recall and F1 score between classes. Weighted means each metric is calculated with respect to how many samples there are in each class. This metric will favour the majority class (e.g. it will give a high value when one class out performs another due to having more samples)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.69      0.82      0.75        33\n",
      "           1       0.84      0.72      0.77        43\n",
      "\n",
      "    accuracy                           0.76        76\n",
      "   macro avg       0.77      0.77      0.76        76\n",
      "weighted avg       0.77      0.76      0.76        76\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression model evaluation metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. **R^2 (pronounced r-squared) or coefficient of determination** - Compares your models predictions to the mean of the targets. Values can range from negative infinity (a very poor model) to 1. For example, if all your model does is predict the mean of the targets, its R^2 value would be 0. And if your model perfectly predicts a range of numbers it's R^2 value would be 1. \n",
    "2. **Mean absolute error (MAE)** - The average of the absolute differences between predictions and actual values. It gives you an idea of how wrong your predictions were.\n",
    "3. **Mean squared error (MSE)** - The average squared differences between predictions and actual values. Squaring the errors removes negative errors. It also amplifies outliers (samples which have larger errors)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "housing = fetch_california_housing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>4.526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>3.585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>3.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "\n",
       "   Longitude  target  \n",
       "0    -122.23   4.526  \n",
       "1    -122.22   3.585  \n",
       "2    -122.24   3.521  \n",
       "3    -122.25   3.413  \n",
       "4    -122.25   3.422  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_df = pd.DataFrame(housing[\"data\"], columns=housing[\"feature_names\"])\n",
    "housing_df[\"target\"] = pd.Series(housing[\"target\"])\n",
    "housing_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the RandomForestRegressor model class from the ensemble module\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# Setup random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Create the data\n",
    "X = housing_df.drop(\"target\", axis=1)\n",
    "y = housing_df[\"target\"]\n",
    "\n",
    "# Split into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "# Institate and fit the model (on the training set)\n",
    "model = RandomForestRegressor()\n",
    "model.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8057655811971304"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the models R^2 score\n",
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3267155795542638"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean absolute error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "y_preds = model.predict(X_test)\n",
    "mae = mean_absolute_error(y_test, y_preds)\n",
    "mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>actual values</th>\n",
       "      <th>predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.47700</td>\n",
       "      <td>0.494300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.45800</td>\n",
       "      <td>0.764200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.00001</td>\n",
       "      <td>4.934686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.18600</td>\n",
       "      <td>2.561130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.78000</td>\n",
       "      <td>2.297640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4123</th>\n",
       "      <td>2.63300</td>\n",
       "      <td>2.240880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4124</th>\n",
       "      <td>2.66800</td>\n",
       "      <td>1.963990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4125</th>\n",
       "      <td>5.00001</td>\n",
       "      <td>4.844759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4126</th>\n",
       "      <td>0.72300</td>\n",
       "      <td>0.716810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4127</th>\n",
       "      <td>1.51500</td>\n",
       "      <td>1.649720</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4128 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      actual values  predictions\n",
       "0           0.47700     0.494300\n",
       "1           0.45800     0.764200\n",
       "2           5.00001     4.934686\n",
       "3           2.18600     2.561130\n",
       "4           2.78000     2.297640\n",
       "...             ...          ...\n",
       "4123        2.63300     2.240880\n",
       "4124        2.66800     1.963990\n",
       "4125        5.00001     4.844759\n",
       "4126        0.72300     0.716810\n",
       "4127        1.51500     1.649720\n",
       "\n",
       "[4128 rows x 2 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data={\"actual values\": y_test, \n",
    "                   \"predictions\": y_preds}).reset_index()\n",
    "\n",
    "df.drop(\"index\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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": [
    "fig, ax = plt.subplots()\n",
    "x = np.arange(0, len(df), 1)\n",
    "ax.scatter(x, df[\"actual values\"], c='b', label=\"Acutual Values\")\n",
    "ax.scatter(x, df[\"predictions\"], c='r', label=\"Predictions\")\n",
    "\n",
    "ax.legend(loc=(1, 0.45));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25452652628268335"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean squared error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "mse = mean_squared_error(y_test, y_preds)\n",
    "mse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# scoring parameter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "X = heart_disease.drop(\"target\", axis=1)\n",
    "y = heart_disease[\"target\"]\n",
    "\n",
    "clf = RandomForestClassifier(n_estimators=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81967213, 0.90163934, 0.83606557, 0.78333333, 0.78333333])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "cv_acc = cross_val_score(clf, X, y, cv=5)\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cross-validated accuracy is: 82.48%\n"
     ]
    }
   ],
   "source": [
    "# Cross-validated accuracy\n",
    "print(f\"The cross-validated accuracy is: {np.mean(cv_acc)*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cross-validated accuracy is: 82.48%\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "cv_acc = cross_val_score(clf, X, y, cv=5, scoring=\"accuracy\")\n",
    "print(f\"The cross-validated accuracy is: {np.mean(cv_acc)*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cross-validated precision is: 0.83\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "cv_precision = cross_val_score(clf, X, y, cv=5, scoring=\"precision\")\n",
    "print(f\"The cross-validated precision is: {np.mean(cv_precision):.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cross-validated recall is: 0.85\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "cv_recall = cross_val_score(clf, X, y, cv=5, scoring=\"recall\")\n",
    "print(f\"The cross-validated recall is: {np.mean(cv_recall):.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cross-validated F1 score is: 0.84\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "cv_f1 = cross_val_score(clf, X, y, cv=5, scoring=\"f1\")\n",
    "print(f\"The cross-validated F1 score is: {np.mean(cv_f1):.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier metrics on the test set:\n",
      "Accuracy: 85.25%\n",
      "Precision: 0.85\n",
      "Recall: 0.88\n",
      "F1: 0.86\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "X = heart_disease.drop(\"target\", axis=1)\n",
    "y = heart_disease[\"target\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "clf = RandomForestClassifier(n_estimators=100)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions\n",
    "y_preds = clf.predict(X_test)\n",
    "\n",
    "# Evaluate the classifier\n",
    "print(\"Classifier metrics on the test set:\")\n",
    "print(f\"Accuracy: {accuracy_score(y_test, y_preds) * 100:.2f}%\")\n",
    "print(f\"Precision: {precision_score(y_test, y_preds):.2f}\")\n",
    "print(f\"Recall: {recall_score(y_test, y_preds):.2f}\")\n",
    "print(f\"F1: {f1_score(y_test, y_preds):.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameter grid RandomizedSearchCV will search over\n",
    "grid = {\"n_estimators\": [10, 100, 200, 500, 1000, 1200],\n",
    "        \"max_depth\": [None, 5, 10, 20, 30],\n",
    "        \"max_features\": [\"auto\", \"sqrt\"],\n",
    "        \"min_samples_split\": [2, 4, 6],\n",
    "        \"min_samples_leaf\": [1, 2, 4]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   1.2s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   1.2s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   1.2s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   1.2s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   1.2s\n",
      "[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=100; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=100; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=100; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=100; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=100; total time=   0.0s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=100; total time=   0.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=100; total time=   0.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=100; total time=   0.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=100; total time=   0.1s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=100; total time=   0.1s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=500; total time=   0.6s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=500; total time=   0.6s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=500; total time=   0.6s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=500; total time=   0.6s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=500; total time=   0.6s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=200; total time=   0.2s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=200; total time=   0.2s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=200; total time=   0.2s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=200; total time=   0.2s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=200; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.2s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1000; total time=   1.3s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1000; total time=   1.3s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1000; total time=   1.4s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1000; total time=   1.3s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1000; total time=   1.3s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=1200; total time=   2.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=1200; total time=   1.8s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=1200; total time=   1.6s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=1200; total time=   1.7s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1200; total time=   1.6s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1200; total time=   1.6s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1200; total time=   1.9s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1200; total time=   1.8s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1200; total time=   1.8s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=6, n_estimators=100; total time=   0.0s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=6, n_estimators=100; total time=   0.1s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=6, n_estimators=100; total time=   0.0s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=6, n_estimators=100; total time=   0.0s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=6, n_estimators=100; total time=   0.1s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=1200; total time=   1.7s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=1200; total time=   1.6s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=500; total time=   0.6s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=500; total time=   0.6s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=500; total time=   0.6s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=500; total time=   0.6s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=500; total time=   0.6s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=100; total time=   0.0s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=100; total time=   0.1s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=100; total time=   0.1s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=100; total time=   0.1s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=100; total time=   0.0s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=4, n_estimators=100; total time=   0.0s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=4, n_estimators=100; total time=   0.0s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=4, n_estimators=100; total time=   0.0s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=4, n_estimators=100; total time=   0.0s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=4, n_estimators=100; total time=   0.0s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=500; total time=   0.6s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=500; total time=   0.6s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=500; total time=   0.6s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=500; total time=   0.7s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=500; total time=   0.8s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=500; total time=   0.6s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=500; total time=   0.7s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=500; total time=   0.7s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=500; total time=   0.7s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=500; total time=   0.6s\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV, train_test_split\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# Split into X & y\n",
    "X = heart_disease.drop(\"target\", axis=1)\n",
    "y = heart_disease[\"target\"]\n",
    "\n",
    "# Split into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "\n",
    "clf = RandomForestClassifier()\n",
    "\n",
    "# Setup RandomizedSearchCV\n",
    "rs_clf = RandomizedSearchCV(estimator=clf,\n",
    "                            param_distributions=grid,\n",
    "                            n_iter=20, # try 20 models total\n",
    "                            cv=5, # 5-fold cross-validation\n",
    "                            verbose=2) # print out results\n",
    "\n",
    "# Fit the RandomizedSearchCV version of clf\n",
    "rs_clf.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 200,\n",
       " 'min_samples_split': 4,\n",
       " 'min_samples_leaf': 4,\n",
       " 'max_features': 'auto',\n",
       " 'max_depth': 10}"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the best hyperparameters found by RandomizedSearchCV\n",
    "rs_clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=10, min_samples_leaf=4, min_samples_split=4,\n",
       "                       n_estimators=200)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = rs_clf.best_estimator_\n",
    "clf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GridSearchCV\n",
    "\n",
    "It searches for everything in the search space and so it takes a long time to complete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_2 = {'n_estimators': [1200, 1500, 2000],\n",
    "          'max_depth': [None, 5, 10],\n",
    "          'max_features': ['auto', 'sqrt'],\n",
    "          'min_samples_split': [4, 6],\n",
    "          'min_samples_leaf': [1, 2]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 72 candidates, totalling 360 fits\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=1200; total time=   2.0s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=1200; total time=   2.0s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=1200; total time=   2.7s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=1200; total time=   1.7s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=1200; total time=   1.7s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=1500; total time=   2.3s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=1500; total time=   2.6s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=1500; total time=   2.7s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=1500; total time=   2.1s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=1500; total time=   2.0s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=2000; total time=   2.6s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=2000; total time=   3.0s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=2000; total time=   3.4s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=2000; total time=   3.6s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=2000; total time=   3.6s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1200; total time=   1.7s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1200; total time=   1.8s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1200; total time=   2.1s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1200; total time=   1.9s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1200; total time=   1.9s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1500; total time=   2.3s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1500; total time=   2.2s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1500; total time=   2.1s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1500; total time=   2.2s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1500; total time=   2.2s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=2000; total time=   2.7s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=2000; total time=   2.7s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=2000; total time=   3.3s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=2000; total time=   2.9s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=2000; total time=   3.2s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1200; total time=   1.7s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1200; total time=   1.8s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1200; total time=   1.8s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1200; total time=   1.8s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1200; total time=   1.6s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1500; total time=   2.3s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1500; total time=   2.2s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1500; total time=   2.2s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1500; total time=   2.2s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1500; total time=   2.6s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=2000; total time=   2.9s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=2000; total time=   2.6s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=2000; total time=   2.7s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=2000; total time=   2.8s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=2000; total time=   2.9s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   1.6s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   1.6s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   1.9s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=1500; total time=   2.0s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=1500; total time=   2.3s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=2000; total time=   3.0s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=2000; total time=   3.0s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=2000; total time=   2.6s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=2000; total time=   2.6s\n",
      "[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=1200; total time=   1.3s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=1200; total time=   1.6s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=2000; total time=   2.7s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=2000; total time=   2.8s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=2000; total time=   2.7s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=2000; total time=   2.9s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=1200; total time=   1.8s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=1200; total time=   1.8s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=1200; total time=   1.6s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=1200; total time=   1.7s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=1500; total time=   2.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=2000; total time=   2.6s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=2000; total time=   2.6s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=2000; total time=   2.4s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=2000; total time=   2.6s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=1500; total time=   2.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=2000; total time=   2.6s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=2000; total time=   2.6s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=1200; total time=   1.4s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=1500; total time=   1.8s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=1500; total time=   1.8s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=2000; total time=   2.8s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=2000; total time=   2.7s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1200; total time=   1.6s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1200; total time=   1.6s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1200; total time=   1.6s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1200; total time=   1.6s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1500; total time=   2.0s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1500; total time=   2.2s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1500; total time=   2.1s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1500; total time=   2.2s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1500; total time=   2.0s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=2000; total time=   3.0s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=2000; total time=   2.8s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=2000; total time=   2.7s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=2000; total time=   2.6s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=2000; total time=   2.7s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1200; total time=   1.6s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1200; total time=   1.6s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1200; total time=   1.6s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1500; total time=   2.0s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1500; total time=   1.8s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=2000; total time=   2.7s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=2000; total time=   3.5s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=2000; total time=   3.0s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=2000; total time=   2.8s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=2000; total time=   2.8s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   1.7s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   1.7s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   1.6s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   1.7s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   1.7s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=1500; total time=   2.3s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=1500; total time=   2.3s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=1500; total time=   2.6s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=2000; total time=   2.6s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=2000; total time=   2.6s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=2000; total time=   2.6s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=2000; total time=   2.8s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=2000; total time=   2.8s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=1200; total time=   1.6s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=1200; total time=   1.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=1200; total time=   1.7s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=1500; total time=   2.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=1500; total time=   2.2s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=1500; total time=   2.3s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=1500; total time=   2.3s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=1500; total time=   2.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=2000; total time=   2.7s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=2000; total time=   2.7s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=2000; total time=   2.6s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=1200; total time=   1.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=1200; total time=   1.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=1200; total time=   1.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=1500; total time=   1.8s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=2000; total time=   2.7s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=2000; total time=   2.7s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1500; total time=   1.8s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1500; total time=   1.8s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=2000; total time=   2.6s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=2000; total time=   2.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=2000; total time=   2.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=1500; total time=   1.8s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=2000; total time=   2.6s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=2000; total time=   2.8s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=2000; total time=   2.9s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=2000; total time=   2.7s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=2000; total time=   2.7s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1200; total time=   1.8s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1200; total time=   1.7s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1200; total time=   1.8s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1200; total time=   1.9s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1500; total time=   2.6s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1500; total time=   2.3s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1500; total time=   2.0s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=2000; total time=   2.9s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=2000; total time=   2.6s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=2000; total time=   2.6s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=2000; total time=   2.6s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=2000; total time=   2.6s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=2000; total time=   2.5s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   1.5s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   1.4s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=1500; total time=   2.0s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=1500; total time=   2.0s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=1500; total time=   1.9s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=6, n_estimators=2000; total time=   2.6s\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_9164/2064398441.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m \u001b[0mgs_clf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Desktop\\ML\\sample\\env\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m             \u001b[1;31m# extra_args > 0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\ML\\sample\\env\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[0;32m    839\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    840\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 841\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    842\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    843\u001b[0m             \u001b[1;31m# multimetric is determined here because in the case of a callable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\ML\\sample\\env\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[1;34m(self, evaluate_candidates)\u001b[0m\n\u001b[0;32m   1294\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1295\u001b[0m         \u001b[1;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1296\u001b[1;33m         \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1297\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1298\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\ML\\sample\\env\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[1;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[0;32m    793\u001b[0m                               n_splits, n_candidates, n_candidates * n_splits))\n\u001b[0;32m    794\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 795\u001b[1;33m                 out = parallel(delayed(_fit_and_score)(clone(base_estimator),\n\u001b[0m\u001b[0;32m    796\u001b[0m                                                        \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    797\u001b[0m                                                        \u001b[0mtrain\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtest\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\ML\\sample\\env\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1042\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1043\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1044\u001b[1;33m             \u001b[1;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1045\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1046\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\ML\\sample\\env\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    857\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    858\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 859\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    860\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    861\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\ML\\sample\\env\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    775\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    776\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 777\u001b[1;33m             \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    778\u001b[0m             \u001b[1;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    779\u001b[0m             \u001b[1;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\ML\\sample\\env\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    206\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    207\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    209\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\ML\\sample\\env\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    570\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    571\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 572\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    574\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\ML\\sample\\env\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    260\u001b[0m         \u001b[1;31m# change the default number of processes to -1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 262\u001b[1;33m             return [func(*args, **kwargs)\n\u001b[0m\u001b[0;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[0;32m    264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\ML\\sample\\env\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    260\u001b[0m         \u001b[1;31m# change the default number of processes to -1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 262\u001b[1;33m             return [func(*args, **kwargs)\n\u001b[0m\u001b[0;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[0;32m    264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\ML\\sample\\env\\lib\\site-packages\\sklearn\\utils\\fixes.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    220\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    221\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mconfig_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 222\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Desktop\\ML\\sample\\env\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[1;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)\u001b[0m\n\u001b[0;32m    596\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    597\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 598\u001b[1;33m             \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    599\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    600\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\ML\\sample\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    385\u001b[0m             \u001b[1;31m# parallel_backend contexts set at a higher level,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    386\u001b[0m             \u001b[1;31m# since correctness does not rely on using threads.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 387\u001b[1;33m             trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n\u001b[0m\u001b[0;32m    388\u001b[0m                              \u001b[1;33m**\u001b[0m\u001b[0m_joblib_parallel_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprefer\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'threads'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    389\u001b[0m                 delayed(_parallel_build_trees)(\n",
      "\u001b[1;32m~\\Desktop\\ML\\sample\\env\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1042\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1043\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1044\u001b[1;33m             \u001b[1;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1045\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1046\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\ML\\sample\\env\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    857\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    858\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 859\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    860\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    861\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\ML\\sample\\env\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    775\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    776\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 777\u001b[1;33m             \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    778\u001b[0m             \u001b[1;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    779\u001b[0m             \u001b[1;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\ML\\sample\\env\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    206\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    207\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    209\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\ML\\sample\\env\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    570\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    571\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 572\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    574\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\ML\\sample\\env\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    260\u001b[0m         \u001b[1;31m# change the default number of processes to -1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 262\u001b[1;33m             return [func(*args, **kwargs)\n\u001b[0m\u001b[0;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[0;32m    264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\ML\\sample\\env\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    260\u001b[0m         \u001b[1;31m# change the default number of processes to -1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 262\u001b[1;33m             return [func(*args, **kwargs)\n\u001b[0m\u001b[0;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[0;32m    264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\ML\\sample\\env\\lib\\site-packages\\sklearn\\utils\\fixes.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    220\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    221\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mconfig_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 222\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Desktop\\ML\\sample\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36m_parallel_build_trees\u001b[1;34m(tree, forest, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight, n_samples_bootstrap)\u001b[0m\n\u001b[0;32m    167\u001b[0m                                                         indices=indices)\n\u001b[0;32m    168\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 169\u001b[1;33m         \u001b[0mtree\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcurr_sample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    170\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    171\u001b[0m         \u001b[0mtree\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\ML\\sample\\env\\lib\\site-packages\\sklearn\\tree\\_classes.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m    901\u001b[0m         \"\"\"\n\u001b[0;32m    902\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 903\u001b[1;33m         super().fit(\n\u001b[0m\u001b[0;32m    904\u001b[0m             \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    905\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\ML\\sample\\env\\lib\\site-packages\\sklearn\\tree\\_classes.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m    200\u001b[0m             \u001b[0my_encoded\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    201\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_outputs_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 202\u001b[1;33m                 classes_k, y_encoded[:, k] = np.unique(y[:, k],\n\u001b[0m\u001b[0;32m    203\u001b[0m                                                        return_inverse=True)\n\u001b[0;32m    204\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclasses_k\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<__array_function__ internals>\u001b[0m in \u001b[0;36munique\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\ML\\sample\\env\\lib\\site-packages\\numpy\\lib\\arraysetops.py\u001b[0m in \u001b[0;36munique\u001b[1;34m(ar, return_index, return_inverse, return_counts, axis)\u001b[0m\n\u001b[0;32m    259\u001b[0m     \u001b[0mar\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masanyarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mar\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    260\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 261\u001b[1;33m         \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_unique1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mar\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_inverse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_counts\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    262\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0m_unpack_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mret\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    263\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\ML\\sample\\env\\lib\\site-packages\\numpy\\lib\\arraysetops.py\u001b[0m in \u001b[0;36m_unique1d\u001b[1;34m(ar, return_index, return_inverse, return_counts)\u001b[0m\n\u001b[0;32m    317\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    318\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0moptional_indices\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 319\u001b[1;33m         \u001b[0mperm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mar\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margsort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'mergesort'\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mreturn_index\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;34m'quicksort'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    320\u001b[0m         \u001b[0maux\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mar\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mperm\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    321\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# Split into X & y\n",
    "X = heart_disease.drop(\"target\", axis=1)\n",
    "y = heart_disease[\"target\"]\n",
    "\n",
    "# Split into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "clf = RandomForestClassifier()\n",
    "\n",
    "# Setup GridSearchCV\n",
    "gs_clf = GridSearchCV(estimator=clf,\n",
    "                      param_grid=grid_2,\n",
    "                      cv=5, # 5-fold cross-validation\n",
    "                      verbose=2) # print out progress\n",
    "\n",
    "\n",
    "gs_clf.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I stopped this because it was taking toooooo long. Use grid search with caution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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