12_GridSearch.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "a818f9cd",
   "metadata": {},
   "source": [
    "https://scikit-learn.org/stable/modules/grid_search.html"
   ]
  },
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   "id": "f60555b5",
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   "source": [
    "### GridSearch : \n",
    "    - Automated hyperparameter tuning "
   ]
  },
  {
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    "    in Random Forest: \n",
    "    \n",
    "    hyperparameters : \n",
    "        n_estimators : number of indivisual DTs(base learners). [100,200,300]\n",
    "        max_depth : max depth of base DTs. [3,5,7,10]\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a86fc6dd",
   "metadata": {},
   "source": [
    "#### Exhaustive Grid Search"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf351555",
   "metadata": {},
   "source": [
    "        \n",
    "        \n",
    "                    n-estimators\n",
    "                    \n",
    "                    100 200 300\n",
    "        Max     3     .  .   .\n",
    "        depth   5     .  .   .\n",
    "                7     .  .   .\n",
    "                10    .  .   .\n",
    "                \n",
    "                \n",
    "          find the best combinations of hyperparamets , by training model with each combination.\n",
    "          \n",
    "          in above case : we train 4*3 = 12 RFs.\n",
    "          \n",
    "          RF1 , RF2 ... RF12\n",
    "          \n",
    "          take the max performing model . take that model's hyperparameters combination.\n",
    "          - it is computationally expensive.\n",
    "          - this will exhaust all the possibilites of Hyperparamer and returns the best result.\n",
    "          \n",
    "          \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aa6950d",
   "metadata": {},
   "source": [
    "#### GridSearchCV : \n",
    "\n",
    "    in case: of k-fold:\n",
    "    lets say: hyperparamets are: \n",
    "    \n",
    "    3 fold cv\n",
    "    4 set of hyperparamets of max_depth\n",
    "    4 set of hyperparamets of n_estiamtors \n",
    "    \n",
    "    total number of model training : 3*4*4 = 48 \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2343d47",
   "metadata": {},
   "source": [
    "#### RandomSearchCV : \n",
    "\n",
    "    takes random combinations of hyperparameters : \n",
    "    \n",
    "    instead of set of hyperparameters , it takes the uniform distribuition , and chose combinations randomly."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "234243c8",
   "metadata": {},
   "source": [
    "https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d49a8243",
   "metadata": {},
   "source": [
    "    sklearn.model_selection.GridSearchCV\n",
    "    class sklearn.model_selection.GridSearchCV(\n",
    "                            estimator, \n",
    "                            param_grid, *,\n",
    "                            scoring=None,           \n",
    "                            n_jobs=None,   #  \n",
    "                            refit=True,\n",
    "                            cv=None,         # cross validation\n",
    "                            verbose=0,\n",
    "                            pre_dispatch='2*n_jobs',\n",
    "                            error_score=nan, \n",
    "                            return_train_score=False)\n",
    "                            \n",
    "                            \n",
    "                            \n",
    "          n_jobsint, default=None\n",
    "    Number of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "299cd05d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8309aeb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {\"max_depth\":[3,5,7,10,15],\n",
    "             \"n_estimators\":[100,200,300],\n",
    "             \"max_features\":[7,10,15,20]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2c0d899a",
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import pandas as pd'); }\n",
       "    "
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       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import pandas as pd'); }\n",
       "    "
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   "source": [
    "X_train = pd.read_csv(\"X_train.csv\")\n",
    "y_train = pd.read_csv(\"y_train.csv\")\n",
    "X_test =  pd.read_csv(\"X_test.csv\")\n",
    "y_test = pd.read_csv(\"y_test.csv\")\n",
    "\n",
    "from imblearn.over_sampling import SMOTE\n",
    "smote = SMOTE()\n",
    "\n",
    "X_smote,y_smote = smote.fit_resample(X_train,y_train)\n",
    "X_smote.shape,y_smote.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "091f5a83",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "RFC = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e2954597",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search = GridSearchCV(\n",
    "    estimator = RFC,\n",
    "    param_grid = parameters,\n",
    "    scoring = \"accuracy\",\n",
    "    n_jobs = -1,        # use all the available processors in the system \n",
    "    refit=True,                   # need not to train again after grid search\n",
    "    cv=3,\n",
    "    pre_dispatch='2*n_jobs',\n",
    "    return_train_score=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "66447e3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3, estimator=RandomForestClassifier(), n_jobs=-1,\n",
       "             param_grid={&#x27;max_depth&#x27;: [3, 5, 7, 10, 15],\n",
       "                         &#x27;max_features&#x27;: [7, 10, 15, 20],\n",
       "                         &#x27;n_estimators&#x27;: [100, 200, 300]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3, estimator=RandomForestClassifier(), n_jobs=-1,\n",
       "             param_grid={&#x27;max_depth&#x27;: [3, 5, 7, 10, 15],\n",
       "                         &#x27;max_features&#x27;: [7, 10, 15, 20],\n",
       "                         &#x27;n_estimators&#x27;: [100, 200, 300]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=3, estimator=RandomForestClassifier(), n_jobs=-1,\n",
       "             param_grid={'max_depth': [3, 5, 7, 10, 15],\n",
       "                         'max_features': [7, 10, 15, 20],\n",
       "                         'n_estimators': [100, 200, 300]},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(X_smote,y_smote.values.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bb34fd07",
   "metadata": {},
   "outputs": [],
   "source": [
    "# grid_search.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b658412d",
   "metadata": {},
   "outputs": [
    {
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       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=10, max_features=7, n_estimators=300)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=10, max_features=7, n_estimators=300)</pre></div></div></div></div></div>"
      ],
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       "RandomForestClassifier(max_depth=10, max_features=7, n_estimators=300)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "grid_search.best_estimator_"
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  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2fc03428",
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    {
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       "{'max_depth': 10, 'max_features': 7, 'n_estimators': 300}"
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    "grid_search.best_params_"
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  {
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    "    we dont need to refit again with best paramets , as we have passed refit=True already in grid search. "
   ]
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       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import pandas as pd'); }\n",
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       "<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>params</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'max_depth': 3, 'max_features': 7, 'n_estimat...</td>\n",
       "      <td>0.839827</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'max_depth': 3, 'max_features': 7, 'n_estimat...</td>\n",
       "      <td>0.838203</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'max_depth': 3, 'max_features': 7, 'n_estimat...</td>\n",
       "      <td>0.831710</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{'max_depth': 3, 'max_features': 10, 'n_estima...</td>\n",
       "      <td>0.817100</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{'max_depth': 3, 'max_features': 10, 'n_estima...</td>\n",
       "      <td>0.821970</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>{'max_depth': 3, 'max_features': 10, 'n_estima...</td>\n",
       "      <td>0.828463</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>{'max_depth': 3, 'max_features': 15, 'n_estima...</td>\n",
       "      <td>0.814935</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>{'max_depth': 3, 'max_features': 15, 'n_estima...</td>\n",
       "      <td>0.816558</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>{'max_depth': 3, 'max_features': 15, 'n_estima...</td>\n",
       "      <td>0.813312</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>{'max_depth': 3, 'max_features': 20, 'n_estima...</td>\n",
       "      <td>0.810065</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>{'max_depth': 3, 'max_features': 20, 'n_estima...</td>\n",
       "      <td>0.810065</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>{'max_depth': 3, 'max_features': 20, 'n_estima...</td>\n",
       "      <td>0.811688</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>{'max_depth': 5, 'max_features': 7, 'n_estimat...</td>\n",
       "      <td>0.870130</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>{'max_depth': 5, 'max_features': 7, 'n_estimat...</td>\n",
       "      <td>0.867424</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>{'max_depth': 5, 'max_features': 7, 'n_estimat...</td>\n",
       "      <td>0.872835</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>{'max_depth': 5, 'max_features': 10, 'n_estima...</td>\n",
       "      <td>0.864177</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>{'max_depth': 5, 'max_features': 10, 'n_estima...</td>\n",
       "      <td>0.868506</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>{'max_depth': 5, 'max_features': 10, 'n_estima...</td>\n",
       "      <td>0.872835</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>{'max_depth': 5, 'max_features': 15, 'n_estima...</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>{'max_depth': 5, 'max_features': 15, 'n_estima...</td>\n",
       "      <td>0.870130</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>{'max_depth': 5, 'max_features': 15, 'n_estima...</td>\n",
       "      <td>0.867965</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>{'max_depth': 5, 'max_features': 20, 'n_estima...</td>\n",
       "      <td>0.868506</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>{'max_depth': 5, 'max_features': 20, 'n_estima...</td>\n",
       "      <td>0.871753</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>{'max_depth': 5, 'max_features': 20, 'n_estima...</td>\n",
       "      <td>0.871212</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>{'max_depth': 7, 'max_features': 7, 'n_estimat...</td>\n",
       "      <td>0.882576</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>{'max_depth': 7, 'max_features': 7, 'n_estimat...</td>\n",
       "      <td>0.886364</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>{'max_depth': 7, 'max_features': 7, 'n_estimat...</td>\n",
       "      <td>0.887446</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>{'max_depth': 7, 'max_features': 10, 'n_estima...</td>\n",
       "      <td>0.890693</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>{'max_depth': 7, 'max_features': 10, 'n_estima...</td>\n",
       "      <td>0.885823</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>{'max_depth': 7, 'max_features': 10, 'n_estima...</td>\n",
       "      <td>0.884740</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>{'max_depth': 7, 'max_features': 15, 'n_estima...</td>\n",
       "      <td>0.884740</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>{'max_depth': 7, 'max_features': 15, 'n_estima...</td>\n",
       "      <td>0.886364</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>{'max_depth': 7, 'max_features': 15, 'n_estima...</td>\n",
       "      <td>0.881494</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>{'max_depth': 7, 'max_features': 20, 'n_estima...</td>\n",
       "      <td>0.883117</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>{'max_depth': 7, 'max_features': 20, 'n_estima...</td>\n",
       "      <td>0.882035</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>{'max_depth': 7, 'max_features': 20, 'n_estima...</td>\n",
       "      <td>0.886905</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>{'max_depth': 10, 'max_features': 7, 'n_estima...</td>\n",
       "      <td>0.894481</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>{'max_depth': 10, 'max_features': 7, 'n_estima...</td>\n",
       "      <td>0.897727</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>{'max_depth': 10, 'max_features': 7, 'n_estima...</td>\n",
       "      <td>0.904221</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>{'max_depth': 10, 'max_features': 10, 'n_estim...</td>\n",
       "      <td>0.895022</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>{'max_depth': 10, 'max_features': 10, 'n_estim...</td>\n",
       "      <td>0.896645</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>{'max_depth': 10, 'max_features': 10, 'n_estim...</td>\n",
       "      <td>0.894481</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>{'max_depth': 10, 'max_features': 15, 'n_estim...</td>\n",
       "      <td>0.897186</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>{'max_depth': 10, 'max_features': 15, 'n_estim...</td>\n",
       "      <td>0.895022</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>{'max_depth': 10, 'max_features': 15, 'n_estim...</td>\n",
       "      <td>0.895563</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>{'max_depth': 10, 'max_features': 20, 'n_estim...</td>\n",
       "      <td>0.891775</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>{'max_depth': 10, 'max_features': 20, 'n_estim...</td>\n",
       "      <td>0.895563</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>{'max_depth': 10, 'max_features': 20, 'n_estim...</td>\n",
       "      <td>0.894481</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>{'max_depth': 15, 'max_features': 7, 'n_estima...</td>\n",
       "      <td>0.899351</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>{'max_depth': 15, 'max_features': 7, 'n_estima...</td>\n",
       "      <td>0.899892</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>{'max_depth': 15, 'max_features': 7, 'n_estima...</td>\n",
       "      <td>0.904221</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>{'max_depth': 15, 'max_features': 10, 'n_estim...</td>\n",
       "      <td>0.900974</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>{'max_depth': 15, 'max_features': 10, 'n_estim...</td>\n",
       "      <td>0.899892</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>{'max_depth': 15, 'max_features': 10, 'n_estim...</td>\n",
       "      <td>0.898268</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>{'max_depth': 15, 'max_features': 15, 'n_estim...</td>\n",
       "      <td>0.902597</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>{'max_depth': 15, 'max_features': 15, 'n_estim...</td>\n",
       "      <td>0.895022</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>{'max_depth': 15, 'max_features': 15, 'n_estim...</td>\n",
       "      <td>0.898268</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>{'max_depth': 15, 'max_features': 20, 'n_estim...</td>\n",
       "      <td>0.899351</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>{'max_depth': 15, 'max_features': 20, 'n_estim...</td>\n",
       "      <td>0.898810</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>{'max_depth': 15, 'max_features': 20, 'n_estim...</td>\n",
       "      <td>0.897727</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               params  mean_test_score  \\\n",
       "0   {'max_depth': 3, 'max_features': 7, 'n_estimat...         0.839827   \n",
       "1   {'max_depth': 3, 'max_features': 7, 'n_estimat...         0.838203   \n",
       "2   {'max_depth': 3, 'max_features': 7, 'n_estimat...         0.831710   \n",
       "3   {'max_depth': 3, 'max_features': 10, 'n_estima...         0.817100   \n",
       "4   {'max_depth': 3, 'max_features': 10, 'n_estima...         0.821970   \n",
       "5   {'max_depth': 3, 'max_features': 10, 'n_estima...         0.828463   \n",
       "6   {'max_depth': 3, 'max_features': 15, 'n_estima...         0.814935   \n",
       "7   {'max_depth': 3, 'max_features': 15, 'n_estima...         0.816558   \n",
       "8   {'max_depth': 3, 'max_features': 15, 'n_estima...         0.813312   \n",
       "9   {'max_depth': 3, 'max_features': 20, 'n_estima...         0.810065   \n",
       "10  {'max_depth': 3, 'max_features': 20, 'n_estima...         0.810065   \n",
       "11  {'max_depth': 3, 'max_features': 20, 'n_estima...         0.811688   \n",
       "12  {'max_depth': 5, 'max_features': 7, 'n_estimat...         0.870130   \n",
       "13  {'max_depth': 5, 'max_features': 7, 'n_estimat...         0.867424   \n",
       "14  {'max_depth': 5, 'max_features': 7, 'n_estimat...         0.872835   \n",
       "15  {'max_depth': 5, 'max_features': 10, 'n_estima...         0.864177   \n",
       "16  {'max_depth': 5, 'max_features': 10, 'n_estima...         0.868506   \n",
       "17  {'max_depth': 5, 'max_features': 10, 'n_estima...         0.872835   \n",
       "18  {'max_depth': 5, 'max_features': 15, 'n_estima...         0.875000   \n",
       "19  {'max_depth': 5, 'max_features': 15, 'n_estima...         0.870130   \n",
       "20  {'max_depth': 5, 'max_features': 15, 'n_estima...         0.867965   \n",
       "21  {'max_depth': 5, 'max_features': 20, 'n_estima...         0.868506   \n",
       "22  {'max_depth': 5, 'max_features': 20, 'n_estima...         0.871753   \n",
       "23  {'max_depth': 5, 'max_features': 20, 'n_estima...         0.871212   \n",
       "24  {'max_depth': 7, 'max_features': 7, 'n_estimat...         0.882576   \n",
       "25  {'max_depth': 7, 'max_features': 7, 'n_estimat...         0.886364   \n",
       "26  {'max_depth': 7, 'max_features': 7, 'n_estimat...         0.887446   \n",
       "27  {'max_depth': 7, 'max_features': 10, 'n_estima...         0.890693   \n",
       "28  {'max_depth': 7, 'max_features': 10, 'n_estima...         0.885823   \n",
       "29  {'max_depth': 7, 'max_features': 10, 'n_estima...         0.884740   \n",
       "30  {'max_depth': 7, 'max_features': 15, 'n_estima...         0.884740   \n",
       "31  {'max_depth': 7, 'max_features': 15, 'n_estima...         0.886364   \n",
       "32  {'max_depth': 7, 'max_features': 15, 'n_estima...         0.881494   \n",
       "33  {'max_depth': 7, 'max_features': 20, 'n_estima...         0.883117   \n",
       "34  {'max_depth': 7, 'max_features': 20, 'n_estima...         0.882035   \n",
       "35  {'max_depth': 7, 'max_features': 20, 'n_estima...         0.886905   \n",
       "36  {'max_depth': 10, 'max_features': 7, 'n_estima...         0.894481   \n",
       "37  {'max_depth': 10, 'max_features': 7, 'n_estima...         0.897727   \n",
       "38  {'max_depth': 10, 'max_features': 7, 'n_estima...         0.904221   \n",
       "39  {'max_depth': 10, 'max_features': 10, 'n_estim...         0.895022   \n",
       "40  {'max_depth': 10, 'max_features': 10, 'n_estim...         0.896645   \n",
       "41  {'max_depth': 10, 'max_features': 10, 'n_estim...         0.894481   \n",
       "42  {'max_depth': 10, 'max_features': 15, 'n_estim...         0.897186   \n",
       "43  {'max_depth': 10, 'max_features': 15, 'n_estim...         0.895022   \n",
       "44  {'max_depth': 10, 'max_features': 15, 'n_estim...         0.895563   \n",
       "45  {'max_depth': 10, 'max_features': 20, 'n_estim...         0.891775   \n",
       "46  {'max_depth': 10, 'max_features': 20, 'n_estim...         0.895563   \n",
       "47  {'max_depth': 10, 'max_features': 20, 'n_estim...         0.894481   \n",
       "48  {'max_depth': 15, 'max_features': 7, 'n_estima...         0.899351   \n",
       "49  {'max_depth': 15, 'max_features': 7, 'n_estima...         0.899892   \n",
       "50  {'max_depth': 15, 'max_features': 7, 'n_estima...         0.904221   \n",
       "51  {'max_depth': 15, 'max_features': 10, 'n_estim...         0.900974   \n",
       "52  {'max_depth': 15, 'max_features': 10, 'n_estim...         0.899892   \n",
       "53  {'max_depth': 15, 'max_features': 10, 'n_estim...         0.898268   \n",
       "54  {'max_depth': 15, 'max_features': 15, 'n_estim...         0.902597   \n",
       "55  {'max_depth': 15, 'max_features': 15, 'n_estim...         0.895022   \n",
       "56  {'max_depth': 15, 'max_features': 15, 'n_estim...         0.898268   \n",
       "57  {'max_depth': 15, 'max_features': 20, 'n_estim...         0.899351   \n",
       "58  {'max_depth': 15, 'max_features': 20, 'n_estim...         0.898810   \n",
       "59  {'max_depth': 15, 'max_features': 20, 'n_estim...         0.897727   \n",
       "\n",
       "    rank_test_score  \n",
       "0                49  \n",
       "1                50  \n",
       "2                51  \n",
       "3                54  \n",
       "4                53  \n",
       "5                52  \n",
       "6                56  \n",
       "7                55  \n",
       "8                57  \n",
       "9                59  \n",
       "10               59  \n",
       "11               58  \n",
       "12               42  \n",
       "13               47  \n",
       "14               38  \n",
       "15               48  \n",
       "16               44  \n",
       "17               38  \n",
       "18               37  \n",
       "19               42  \n",
       "20               46  \n",
       "21               44  \n",
       "22               40  \n",
       "23               41  \n",
       "24               34  \n",
       "25               28  \n",
       "26               26  \n",
       "27               25  \n",
       "28               30  \n",
       "29               31  \n",
       "30               32  \n",
       "31               28  \n",
       "32               36  \n",
       "33               33  \n",
       "34               35  \n",
       "35               27  \n",
       "36               21  \n",
       "37               12  \n",
       "38                1  \n",
       "39               19  \n",
       "40               15  \n",
       "41               21  \n",
       "42               14  \n",
       "43               19  \n",
       "44               16  \n",
       "45               24  \n",
       "46               16  \n",
       "47               21  \n",
       "48                7  \n",
       "49                5  \n",
       "50                1  \n",
       "51                4  \n",
       "52                5  \n",
       "53               10  \n",
       "54                3  \n",
       "55               18  \n",
       "56               10  \n",
       "57                7  \n",
       "58                9  \n",
       "59               12  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = pd.DataFrame(grid_search.cv_results_).iloc[:,4:]\n",
    "r[[\"params\",\"mean_test_score\",\"rank_test_score\"]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81693bc4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "82b35eab",
   "metadata": {},
   "source": [
    "### Fine tuning : \n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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    }
   },
   "cell_type": "markdown",
   "id": "41def1ae",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7ffd353",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98a1c4f1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a46ad283",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7472d8d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41ed8e18",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3d1dc84",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbe302f3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8970795",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd1a50a6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}