2 - Grid search CV e CV Aninhada.ipynb

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   "cell_type": "markdown",
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   "source": [
    "<h1>Conceito</h1>\n",
    "\n",
    "<h2><i>Grid search cross validation</i></h2>\n",
    "\n",
    "A biblioteca *SKLearn* possuí uma classe chamada *grid search cross validation*. Através da mesma, é possível definir um *grid* com diversos valores de diferentes hiperparâmetros a serem testados pela classe.\n",
    "\n",
    "O que a classe faz portanto é realizar testes de validação cruzada (ou seja, divide os dados passados a ela em diferentes partes de treino e teste) para cada combinação de parâmetros, resultando assim num *score* médio e seus respectivos desvio padrão para cada combinação.\n",
    "\n",
    "<h2>Validação cruzada aninhada</h2>\n",
    "\n",
    "Uma prática interessante para, novamente, evitar o *overfitting* é a validação cruzada aninhada. Isso acontece quando são realizados dois ou mais *loops* de validação cruzada, um dentro do outro.\n",
    "\n",
    "Um exemplo comum de validação cruzada aninhada é **combinar o *grid search cross validation*, que seria o *loop* interno, com uma validação cruzada 'padrão', que seria o *loop* externo**. Neste caso, o que acontece na prática é o seguinte:\n",
    "\n",
    "1. A validação cruzada padrão (*loop* externo), irá dividir os dados em **dados de treino externos** e **dados de teste externos**, através do método de divisão definido para o *loop* externo\n",
    "2. Os **dados de teste externos** serão 'guardados' e os **dados de treino externos** serão passados para o *gscv* (*loop* interno)\n",
    "3. Os **dados de treino externos** serão dividos em **dados de treino internos** e **dados de teste internos**, através do método de divisão definido para o *loop* interno\n",
    "4. O *gscv* será treinado utilizando os **dados de treino internos** e será calculado o seu *score* utilizando os **dados de teste internos**\n",
    "5. Os passos 3 e 4 serão repetidos para cada iteração do *loop* interno\n",
    "6. É criado um modelo então com a combinação de hiperparâmetros com melhor *score* médio considerando todas as iterações. Esse modelo é treinado com todos os **dados de treino externos** e é passado para o parâmetro ***best_estimator_***\n",
    "7. O *loop* externo então irá calcular o *score* utilizando o **melhor estimador** (definido no passo anterior) e os **dados de teste externos**\n",
    "8. Os passos 1 a 7 são repetidos para cada iteração do *loop* externo"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Aplicação</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando a base de dados\n",
    "dados = pd.read_csv(r'Dados\\base.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dividindo inputs e outputs\n",
    "x = dados[['preco', 'idade_do_modelo', 'km_por_ano']].values\n",
    "y = dados['vendido'].values.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definindo um grid de hiperparâmetros a serem testados\n",
    "\n",
    "grid_hiperparam = {\n",
    "    \"max_depth\" : [3, 5],\n",
    "    \"min_samples_split\": [32, 64, 128],\n",
    "    \"min_samples_leaf\": [32, 64, 128],\n",
    "    \"criterion\": [\"gini\", \"entropy\"]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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=KFold(n_splits=10, random_state=None, shuffle=True),\n",
       "             estimator=DecisionTreeClassifier(),\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;], &#x27;max_depth&#x27;: [3, 5],\n",
       "                         &#x27;min_samples_leaf&#x27;: [32, 64, 128],\n",
       "                         &#x27;min_samples_split&#x27;: [32, 64, 128]})</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=KFold(n_splits=10, random_state=None, shuffle=True),\n",
       "             estimator=DecisionTreeClassifier(),\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;], &#x27;max_depth&#x27;: [3, 5],\n",
       "                         &#x27;min_samples_leaf&#x27;: [32, 64, 128],\n",
       "                         &#x27;min_samples_split&#x27;: [32, 64, 128]})</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: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</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\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=KFold(n_splits=10, random_state=None, shuffle=True),\n",
       "             estimator=DecisionTreeClassifier(),\n",
       "             param_grid={'criterion': ['gini', 'entropy'], 'max_depth': [3, 5],\n",
       "                         'min_samples_leaf': [32, 64, 128],\n",
       "                         'min_samples_split': [32, 64, 128]})"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Realizando a busca melhor modelo através da variação dos hiperparâmetros:\n",
    "SEED = 20\n",
    "np.random.seed(SEED)\n",
    "\n",
    "gscv = GridSearchCV(\n",
    "    DecisionTreeClassifier(),\n",
    "    grid_hiperparam,\n",
    "    cv = KFold(n_splits=10, shuffle=True)\n",
    ")\n",
    "\n",
    "gscv.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'gini',\n",
       " 'max_depth': 3,\n",
       " 'min_samples_leaf': 32,\n",
       " 'min_samples_split': 32}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# É possível verificar os melhores parâmetros através do seguinte atributo da classe:\n",
    "gscv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# É possível verificar todos os resultados através do seguinte atributo da classe:\n",
    "resultados = pd.DataFrame(gscv.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_criterion</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>param_min_samples_leaf</th>\n",
       "      <th>param_min_samples_split</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>split5_test_score</th>\n",
       "      <th>split6_test_score</th>\n",
       "      <th>split7_test_score</th>\n",
       "      <th>split8_test_score</th>\n",
       "      <th>split9_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.021750</td>\n",
       "      <td>0.005179</td>\n",
       "      <td>0.002002</td>\n",
       "      <td>0.000953</td>\n",
       "      <td>gini</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 3, 'min_sam...</td>\n",
       "      <td>0.761</td>\n",
       "      <td>...</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.786</td>\n",
       "      <td>0.804</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.799</td>\n",
       "      <td>0.7869</td>\n",
       "      <td>0.011113</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.038951</td>\n",
       "      <td>0.016680</td>\n",
       "      <td>0.006250</td>\n",
       "      <td>0.004993</td>\n",
       "      <td>gini</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>64</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 3, 'min_sam...</td>\n",
       "      <td>0.761</td>\n",
       "      <td>...</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.786</td>\n",
       "      <td>0.804</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.799</td>\n",
       "      <td>0.7869</td>\n",
       "      <td>0.011113</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.037600</td>\n",
       "      <td>0.022653</td>\n",
       "      <td>0.007950</td>\n",
       "      <td>0.016060</td>\n",
       "      <td>gini</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>128</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 3, 'min_sam...</td>\n",
       "      <td>0.761</td>\n",
       "      <td>...</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.786</td>\n",
       "      <td>0.804</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.799</td>\n",
       "      <td>0.7869</td>\n",
       "      <td>0.011113</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.025149</td>\n",
       "      <td>0.009664</td>\n",
       "      <td>0.002201</td>\n",
       "      <td>0.001308</td>\n",
       "      <td>gini</td>\n",
       "      <td>3</td>\n",
       "      <td>64</td>\n",
       "      <td>32</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 3, 'min_sam...</td>\n",
       "      <td>0.761</td>\n",
       "      <td>...</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.786</td>\n",
       "      <td>0.804</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.799</td>\n",
       "      <td>0.7869</td>\n",
       "      <td>0.011113</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.012899</td>\n",
       "      <td>0.002343</td>\n",
       "      <td>0.001301</td>\n",
       "      <td>0.000641</td>\n",
       "      <td>gini</td>\n",
       "      <td>3</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 3, 'min_sam...</td>\n",
       "      <td>0.761</td>\n",
       "      <td>...</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.786</td>\n",
       "      <td>0.804</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.799</td>\n",
       "      <td>0.7869</td>\n",
       "      <td>0.011113</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 22 columns</p>\n",
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      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time   \n",
       "0       0.021750      0.005179         0.002002        0.000953  \\\n",
       "1       0.038951      0.016680         0.006250        0.004993   \n",
       "2       0.037600      0.022653         0.007950        0.016060   \n",
       "3       0.025149      0.009664         0.002201        0.001308   \n",
       "4       0.012899      0.002343         0.001301        0.000641   \n",
       "\n",
       "  param_criterion param_max_depth param_min_samples_leaf   \n",
       "0            gini               3                     32  \\\n",
       "1            gini               3                     32   \n",
       "2            gini               3                     32   \n",
       "3            gini               3                     64   \n",
       "4            gini               3                     64   \n",
       "\n",
       "  param_min_samples_split                                             params   \n",
       "0                      32  {'criterion': 'gini', 'max_depth': 3, 'min_sam...  \\\n",
       "1                      64  {'criterion': 'gini', 'max_depth': 3, 'min_sam...   \n",
       "2                     128  {'criterion': 'gini', 'max_depth': 3, 'min_sam...   \n",
       "3                      32  {'criterion': 'gini', 'max_depth': 3, 'min_sam...   \n",
       "4                      64  {'criterion': 'gini', 'max_depth': 3, 'min_sam...   \n",
       "\n",
       "   split0_test_score  ...  split3_test_score  split4_test_score   \n",
       "0              0.761  ...               0.79              0.786  \\\n",
       "1              0.761  ...               0.79              0.786   \n",
       "2              0.761  ...               0.79              0.786   \n",
       "3              0.761  ...               0.79              0.786   \n",
       "4              0.761  ...               0.79              0.786   \n",
       "\n",
       "   split5_test_score  split6_test_score  split7_test_score  split8_test_score   \n",
       "0              0.804              0.789              0.777              0.785  \\\n",
       "1              0.804              0.789              0.777              0.785   \n",
       "2              0.804              0.789              0.777              0.785   \n",
       "3              0.804              0.789              0.777              0.785   \n",
       "4              0.804              0.789              0.777              0.785   \n",
       "\n",
       "   split9_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "0              0.799           0.7869        0.011113                1  \n",
       "1              0.799           0.7869        0.011113                1  \n",
       "2              0.799           0.7869        0.011113                1  \n",
       "3              0.799           0.7869        0.011113                1  \n",
       "4              0.799           0.7869        0.011113                1  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultados.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>DecisionTreeClassifier(max_depth=3, min_samples_leaf=32, min_samples_split=32)</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\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=3, min_samples_leaf=32, min_samples_split=32)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=3, min_samples_leaf=32, min_samples_split=32)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# É possível também obter diretamente o melhor estimador através do seguinte atributo:\n",
    "\n",
    "gscv.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 20\n",
    "np.random.seed(SEED)\n",
    "\n",
    "cv_score = cross_val_score(gscv, x, y, cv = KFold(n_splits=10, shuffle=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.761, 0.789, 0.789, 0.79 , 0.786, 0.804, 0.789, 0.777, 0.785,\n",
       "       0.799])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando o score da cross validation para cada iteração:\n",
    "cv_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "O score final do modelo, após a otimização de hiperparâmetros e a validação cruzada, está entre o intervalo [76.47%, 80.91%] e o seu valor médio é 78.69%\n"
     ]
    }
   ],
   "source": [
    "print(f'O score final do modelo, após a otimização de hiperparâmetros e a validação cruzada, está entre o intervalo [{(cv_score.mean() - 2*cv_score.std())*100:.2f}%, {(cv_score.mean() + 2*cv_score.std())*100:.2f}%] e o seu valor médio é {cv_score.mean()*100:.2f}%')"
   ]
  }
 ],
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