proj1-fsi-2022_1-202033202.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparação do ambiente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn import tree\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.metrics import plot_roc_curve\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Código usado nessa plotagem, com modificações: https://stackoverflow.com/questions/19233771/sklearn-plot-confusion-matrix-with-labels\n",
    "\n",
    "def plot_confusion_matrixes(matrixes, n=1):\n",
    "    plt.rcParams[\"figure.figsize\"] = (8,3.8)\n",
    "    plt.rcParams[\"figure.dpi\"] = (70)\n",
    "    \n",
    "    fig = plt.figure()\n",
    "    fig.subplots_adjust(hspace=0.4, wspace=0.4)\n",
    "    \n",
    "    if n > 1:\n",
    "        for i in range(len(matrixes)):\n",
    "            ax_index = int((i % 2) != 0)\n",
    "            \n",
    "            if ax_index == 0:\n",
    "                fig, ax = plt.subplots(1,2)\n",
    "                sns.heatmap(matrixes[i], annot=True, fmt='g', ax=ax[ax_index], cbar=False)\n",
    "\n",
    "                ax[ax_index].set_xlabel('Predicted labels')\n",
    "                ax[ax_index].set_ylabel('True labels')\n",
    "                ax[ax_index].set_title('Confusion Matrix: Fold ' + str(i))\n",
    "                ax[ax_index].xaxis.set_ticklabels(['Carrier', 'Not carrier'])\n",
    "                ax[ax_index].yaxis.set_ticklabels(['Carrier', 'Not carrier'])\n",
    "\n",
    "            else:\n",
    "                sns.heatmap(matrixes[i], annot=True, fmt='g', ax=ax[ax_index], cbar=False)\n",
    "\n",
    "                ax[ax_index].set_xlabel('Predicted labels')\n",
    "                ax[ax_index].set_ylabel('True labels')\n",
    "                ax[ax_index].set_title('Confusion Matrix: Fold ' + str(i))\n",
    "                ax[ax_index].xaxis.set_ticklabels(['Carrier', 'Not carrier'])\n",
    "                ax[ax_index].yaxis.set_ticklabels(['Carrier', 'Not carrier'])\n",
    "        \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploração inicial dos dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Carregamento dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataset utilizado: https://github.com/zahangirbd/medical_data_for_classification/tree/master/data/Hepatitis\n",
    "\n",
    "train = pd.read_csv(\"./data/hepatitis.data.train.csv\")\n",
    "test = pd.read_csv(\"./data/hepatitis.data.test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataset original vem já com divisão de treino e teste, mas que é desnecessária aqui\n",
    "\n",
    "data = pd.concat([train, test], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tratamento dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical = [\"AGE\", \"BILIRUBIN\", \"ALK_PHOSPHATE\", \"SGOT\", \"ALBUMIN\",\n",
    "            \"PROTIME\"]\n",
    "categorical = [\"SEX\", \"STEROID\", \"ANTIVIRALS\", \"FATIGUE\", \"MALAISE\",\n",
    "              \"ANOREXIA\", \"LIVER_BIG\", \"LIVER_FIRM\", \"SPLEEN_PALPABLE\",\n",
    "              \"SPIDERS\", \"ASCITES\", \"VARICES\", \"HISTOLOGY\"]\n",
    "classifier = \"CLASS\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visão geral de dados faltantes na base\n",
    "\n",
    "missing_data_overview = pd.DataFrame((data[numerical + categorical] == '?').sum()).rename(columns={0: 'missing_data'})\n",
    "missing_data_overview['missing_prop'] = round(100*(missing_data_overview['missing_data']/len(data)), 2)\n",
    "missing_data_overview = missing_data_overview.sort_values('missing_prop', ascending=False)\n",
    "\n",
    "missing_data_overview['type'] = missing_data_overview.index.isin(numerical)\n",
    "missing_data_overview['type'] = missing_data_overview['type'].replace(True, 'numerical').replace(False, 'categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>missing_data</th>\n",
       "      <th>missing_prop</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PROTIME</th>\n",
       "      <td>67</td>\n",
       "      <td>43.23</td>\n",
       "      <td>numerical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALK_PHOSPHATE</th>\n",
       "      <td>29</td>\n",
       "      <td>18.71</td>\n",
       "      <td>numerical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALBUMIN</th>\n",
       "      <td>16</td>\n",
       "      <td>10.32</td>\n",
       "      <td>numerical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVER_FIRM</th>\n",
       "      <td>11</td>\n",
       "      <td>7.10</td>\n",
       "      <td>categorical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVER_BIG</th>\n",
       "      <td>10</td>\n",
       "      <td>6.45</td>\n",
       "      <td>categorical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILIRUBIN</th>\n",
       "      <td>6</td>\n",
       "      <td>3.87</td>\n",
       "      <td>numerical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPIDERS</th>\n",
       "      <td>5</td>\n",
       "      <td>3.23</td>\n",
       "      <td>categorical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VARICES</th>\n",
       "      <td>5</td>\n",
       "      <td>3.23</td>\n",
       "      <td>categorical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASCITES</th>\n",
       "      <td>5</td>\n",
       "      <td>3.23</td>\n",
       "      <td>categorical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPLEEN_PALPABLE</th>\n",
       "      <td>5</td>\n",
       "      <td>3.23</td>\n",
       "      <td>categorical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SGOT</th>\n",
       "      <td>4</td>\n",
       "      <td>2.58</td>\n",
       "      <td>numerical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FATIGUE</th>\n",
       "      <td>1</td>\n",
       "      <td>0.65</td>\n",
       "      <td>categorical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STEROID</th>\n",
       "      <td>1</td>\n",
       "      <td>0.65</td>\n",
       "      <td>categorical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALAISE</th>\n",
       "      <td>1</td>\n",
       "      <td>0.65</td>\n",
       "      <td>categorical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANOREXIA</th>\n",
       "      <td>1</td>\n",
       "      <td>0.65</td>\n",
       "      <td>categorical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>numerical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANTIVIRALS</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>categorical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEX</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>categorical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HISTOLOGY</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>categorical</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 missing_data  missing_prop         type\n",
       "PROTIME                    67         43.23    numerical\n",
       "ALK_PHOSPHATE              29         18.71    numerical\n",
       "ALBUMIN                    16         10.32    numerical\n",
       "LIVER_FIRM                 11          7.10  categorical\n",
       "LIVER_BIG                  10          6.45  categorical\n",
       "BILIRUBIN                   6          3.87    numerical\n",
       "SPIDERS                     5          3.23  categorical\n",
       "VARICES                     5          3.23  categorical\n",
       "ASCITES                     5          3.23  categorical\n",
       "SPLEEN_PALPABLE             5          3.23  categorical\n",
       "SGOT                        4          2.58    numerical\n",
       "FATIGUE                     1          0.65  categorical\n",
       "STEROID                     1          0.65  categorical\n",
       "MALAISE                     1          0.65  categorical\n",
       "ANOREXIA                    1          0.65  categorical\n",
       "AGE                         0          0.00    numerical\n",
       "ANTIVIRALS                  0          0.00  categorical\n",
       "SEX                         0          0.00  categorical\n",
       "HISTOLOGY                   0          0.00  categorical"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data_overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_mod = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08387096774193549"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Porcentagem de dados que serão perdidos com a escolha de remover linhas com dados categóricos ausentes\n",
    "\n",
    "(data_mod[categorical] == '?').any(axis=\"columns\").sum()/len(data_mod)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remoção das linhas com dados categóricos faltantes\n",
    "\n",
    "for cat in categorical:\n",
    "    data_mod = data_mod.drop(index=data_mod[data_mod[cat] == '?'].index)\n",
    "    data_mod[cat] = data_mod[cat].astype(int)\n",
    "    data_mod[cat] = pd.get_dummies(data_mod[cat], drop_first=True).iloc[:, 0].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Para os dados numéricos, valores faltantes foram zerados\n",
    "\n",
    "data_mod[numerical] = data_mod[numerical].replace('?', '0').astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Os dados categóricos são binários, podendo ser transformados em dummy data\n",
    "# Com isso, é possível passar nos modelos do scikit-learn, que não aceitam valores categóricos\n",
    "\n",
    "for cat in categorical:\n",
    "    data_mod = data_mod.drop(index=data_mod[data_mod[cat] == '?'].index)\n",
    "    data_mod[cat] = data_mod[cat].astype(int)\n",
    "    data_mod[cat] = pd.get_dummies(data_mod[cat], drop_first=True).iloc[:, 0].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualização dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x560 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualização de média e desvio padrão dos dados\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(20,8))\n",
    "\n",
    "ax1.set_title('Mean', size=15)\n",
    "ax1.bar(data_mod.mean().keys(), data_mod.mean().values)\n",
    "ax1.xaxis.set_tick_params(rotation=90)\n",
    "\n",
    "ax2.set_title('Standard Deviation', size=15)\n",
    "ax2.bar(data_mod.std().keys(), data_mod.std().values)\n",
    "ax2.xaxis.set_tick_params(rotation=90)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Treinamento de modelos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_keys = numerical + categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data_mod[feature_keys].to_numpy()\n",
    "y = data_mod[classifier].to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision tree (CART)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_tree = tree.DecisionTreeClassifier()\n",
    "cv_tree = StratifiedKFold(n_splits=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 560x266 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cms_tree = []\n",
    "f1s_tree = []\n",
    "\n",
    "# Código usado para plotar a curva ROC de validação cruzada, com modificações: https://stackoverflow.com/questions/66033370/plot-roc-curve-from-cross-validation\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (8,3.8)\n",
    "plt.rcParams[\"figure.dpi\"] = (70)\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "for i, (train, test) in enumerate(cv_tree.split(X, y)):\n",
    "    model_tree.fit(X[train], y[train])\n",
    "    viz = plot_roc_curve(model_tree, X[test], y[test],\n",
    "                         name='ROC fold {}'.format(i+1),\n",
    "                         alpha=1, lw=1, ax=ax)\n",
    "\n",
    "    y_pred = model_tree.predict(X[test])\n",
    "    cms_tree.append(confusion_matrix(y[test], y_pred))\n",
    "    f1s_tree.append(f1_score(y[test], y_pred))\n",
    "\n",
    "ax.set(xlim=[-0.05, 1.05], ylim=[-0.05, 1.05],\n",
    "       title=\"CART Decision Tree on hepatitits data\")\n",
    "ax.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 560x266 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 560x266 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 560x266 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 560x266 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 560x266 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 560x266 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrixes(cms_tree, len(cms_tree))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "O f1 médio da validação cruzada do modelo de Decision Tree é de: 0.350\n"
     ]
    }
   ],
   "source": [
    "print(\"O f1 médio da validação cruzada do modelo de Decision Tree é de: {0:.3f}\".format(np.mean(f1s_tree)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest (m=19)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_forest_19 = RandomForestClassifier(n_estimators=100, max_features=19)\n",
    "cv_forest_19 = StratifiedKFold(n_splits=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 560x266 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cms_forest_19 = []\n",
    "f1s_forest_19 = []\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (8,3.8)\n",
    "plt.rcParams[\"figure.dpi\"] = (70)\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "for i, (train, test) in enumerate(cv_forest_19.split(X, y)):\n",
    "    model_forest_19.fit(X[train], y[train])\n",
    "    viz = plot_roc_curve(model_forest_19, X[test], y[test],\n",
    "                         name='ROC fold {}'.format(i+1),\n",
    "                         alpha=1, lw=1, ax=ax)\n",
    "\n",
    "    y_pred = model_forest_19.predict(X[test])\n",
    "    cms_forest_19.append(confusion_matrix(y[test], y_pred))\n",
    "    f1s_forest_19.append(f1_score(y[test], y_pred))\n",
    "\n",
    "ax.set(xlim=[-0.05, 1.05], ylim=[-0.05, 1.05],\n",
    "       title=\"Random Forest (m=19) on hepatitits data\")\n",
    "ax.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 560x266 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DfhQRd+QdmJnVxjlrVm6dnUbuIeB9skNXz+QXjpk1iHPWrKQ6M6p5D+BZ4HjgBGCipN3zDszMauOcNSu3zuzxnglsEhFvA0haFngQfz3BrKycs2Yl1pnBVTOAaRX3p6d1ZlZOM3DOmpVWtbmaj0yLzwF/kXQD2fmirwOTCojNzBaBc9asa6h2qHlY+vlaurVMOXdbjvGYWe2cs2ZdQLW5mk8vMhAzq49z1qxr6HBwlaQVgVOANamYaD0iNskxLjOrkXPWrNw6M7jqCuAWsrleDyIbHXlnnkGZWV2cs2Yl1pnCOzAibgEiIiZFxInADjnHZWa1c86alVhnvsc7X5KAlyUdCrwBDMw3LDOrg3PWrMQUEdWfIG1K9lWEpYEzgEHAqIh4OM/AevVZvnpgVoi5U8Y1OwRLeg9dVZ15XjNy1vlaHs7Z8mgvZztzkYRH0uJsYGQDYzKzHDhnzcqt2gQaY0nX82xLROyZS0RmVhPnrFnXUG2P98LCojCzRnDOmnUB1SbQeKDIQMysPs5Zs66hM18nMjMzswZx4TUzMytQpwqvpKUkbZiWJckF26zEnLNm5dVhMkraB7gPGJtWrYWvdmJWWs5Zs3LrzKfgk4AvAjMBImISMDzPoMysLs5ZsxLrTOGdHxHzWu6kQ1Yf5heSmdXJOWtWYp0pvOMkHQ/0lbQl8Dt82MqszJyzZiXW2UPNs8jmfj2O7NzRf+UYk5nVxzlrVmIdXiShWTzpejl4wvXy6OxFEprB+VoeztnyqPkiCZJepY35XyNi1QbEZWYN5pw1K7fOXI93nYrlvsDuwAr5hGNmDeCcNSuxDs/xRsT7FbdpEfFLYJcCYjOzGjhnzcqtM4ead6y42wNYH/hnbhGZWV2cs2bl1plDzXtULC8AXgd2yyccM2sA56xZiVUtvOmL9y9GxI8LisfM6uCcNSu/qud4I+JD/EnZrMtwzpqVX7t7vJL+HBFbAE9Kuhq4DpjT8nhE3FpAfGbWSc5Zs66h2qHm/ulnP2AusBPZdwOVfjqJzcrFOWvWBVQrvMMkHQk8zsfJ28Kz1JiVj3PWrAuoVnh7AUP5ZPK2cBKblY9z1qwLqFZ434yIM2ppNI2s3DYi7qktLDOrgXPWrAuoNqq55gnZ08jKUbVub2Y1cc6adQHVCu8mdbZ9p6QjJA2V1L/lVmebZtY+56xZF9DuoeaIqHeKub3Tz5P45MhKXyHFLAfOWbOuoTNTRtYkIlbJq20zazznrFkxOrw6Ua0kDZE0StLv0v3VJe3R0XZm1hzOWbNi5FZ4gd8Az/HxtUFfBU7JsT8zq49z1qwAeRbe4RHxa2AhQETMBz7MsT8zq49z1qwAeRbe9yUNIX1xX9IGwKwc+zOz+jhnzQqQ2+Aq4ATgRmAVSfcAKwJ75difmdXHOWtWgDxHNT8haTtgdbI96+cb8HUHM8uJc9asGA0vvJL+LSLGS9qx1UMrSvKlycxKxjlrVqw89ni/AowH2voagi9NZlY+zlmzAjW88EbEf6cJ11+MiB83un0zayznrFmxchnVnCZc3y2Pts2s8ZyzZsXJc1Tzk5KuBq4D5rSs9Pkis9JyzpoVIM/C2w+YC+xUsc7ni8zKyzlrVoBcCm86XzQ7Io7Oo30zayznrFlx8jzHu76knnm0b2aN5Zw1K06eh5pfAu6XdBOfPF80Osc+zax2zlmzAuQ5V/OrwN3AAGBYxa1b2GnH7Zj07IM8N+nPHHzQPs0Op1s55gdnsPkOe/DdH54FwNx58/j2Caewyz7fYrd9D+eqsTc1OcLS6rY5e93Yy5j61iSuufrSZofS7bTOV4CzzvsFW+28D3sefEwTI8uPIqLZMbSpV5/lyxlYJ/Ts2ZNnJt7PdtvvwcyZ7/HoI7ez5Va7MW3a9GaHtsjmThnX7BAW2aNPTmTOnDncdNvdnH/2ycydN49nn3uRjdcfwZw5c9nrkGP4xU9OZ6UVhjc71EXSe+iqanYM7enK+Qqw9VabMXDgkuy//x7stfdhzQ6nLl0tZ1vnK8CTEyfRt08fTh91Adf+6udNjrB27eVsbnu8klaUdKmkcZIebbnl1V+ZbLLx+kye/AJTpvyD99+fw+2338f222/V7LC6jU02GEH//v0/ut9viSXYeP0RAPTv34+VV1qBqe9Ma1Z4pdWdc/aBBx9i1qzZzQ6jW2qdrwAbjFibIYMHNSmi/OV5qPkK4BZgKeAg4EHgzhz7K43lhn+KN974x0f3p0x5k+WHf7qJEVmLN9+ayosvv8qaq3+u2aGUUbfNWbMi5Vl4B0bELUBExKSIOBHYIcf+zKqaP38+J576Y0446hD691ui2eGUkXPWrAB5jmqeL0nAy5IOBd4ABlbbID3vUAD1HEyPHgNyDC8/b055i+WX/3gPd/jw5Xjs8QlNjMgigh+ceR5bbbYxX9l2y2aHU1aLlLOLS76aFS3PPd7vko2OPAbYgixBD6y2QURcFhFfjIgvduUkfvSxCay91hoMH/5pBgzozw47bMuddz7Q7LC6tZ9d/Gv6LdGXw0d6hHkVi5Szi0u+mhXNo5pzsvPO2zPqnFPpIXHueRdx2eVXNTukmnS1EZIAhx77A1546RXmzv2AwYOWZNRp/8mBR32Pz668Er16ZQd5jj/yYP5t0w2bHOmi8ajm/Nxx29WMGLEWAwb0Z9q0Gey9z+E8/MgTzQ6rJl0tZ1vn63ln/ZCxN97K+EefYMbMWSyz1GBOOuZwvvqlrnekqr2cza3wSroX+I+ImJHuLwWMjYjtOrN9V0/kxUVXS+LFWd6Ft56cdb6Wh3O2PAr/OhGwdEsCA0TEdGBojv2ZWX2cs2YFyLPwzpW0ZssdSWsBH+TYn5nVxzlrVoA8RzUfB9ws6VVAwGeA/XLsz8zqcxzOWbPc5VZ4I+KR9Il5jbTq+Yj4Z179mVl9nLNmxchzj5eUtM/k2YeZNY5z1ix/eZ7jNTMzs1ZceM3MzAqU59WJbuzMOjMrB+esWTEafo5XUi+gD7CypH5koyMBBgGrN7o/M6uPc9asWHkMrjqK7GsJw4FJfJzE7wGjc+jPzOrjnDUrUJ5TRh4ZETUnraegKwdPP1ceBUwZWXPOOl/LwzlbHu3lbJ5fJ7pc0nFAy8zWDwAXR8T8HPs0s9o5Z80KkOeo5kuA1YBz02014NIc+zOz+jhnzQqQ5x7v+hHxhYr7D0l6Ksf+zKw+zlmzAuS5xztP0kYtdyRtiCdcNysz56xZAfLc4z0CGCOpD9koyXnAQTn2Z2b1cc6aFSDPiyQ8CYyQNDjdn5lXX2ZWP+esWTHymEDjgHbWAxARVzS6TzOrnXPWrFh57PGu28a6nsA3gOUAJ7FZuThnzQrU8MIbEd9rWU7nig4BjgXuAs5pdH9mVh/nrFmxcjnHK2kA2TR0hwG3ANtFxN/z6MvM6uecNStOHud4zwD2Ba4CNo2Idxvdh5k1jnPWrFgNn6tZ0ofAbLLv/1U2LiAiYtnOtOO5X8vB876WR15zNTciZ52v5eGcLY/C5mqOiDwn5TCzBnPOmhXLCWdmZlYgF14zM7MCufCamZkVyIXXzMysQC68ZmZmBXLhNTMzK5ALr5mZWYFceM3MzArkwmtmZlYgF14zM7MCufCamZkVyIXXzMysQC68ZmZmBXLhNTMzK5ALr5mZWYFceM3MzArkwmtmZlYgF14zM7MCufCamZkVyIXXzMysQC68ZmZmBXLhNTMzK5AiotkxLLYkHRoRlzU7DvN7YZ3jv5NyWNzfB+/x5uvQZgdgH/F7YZ3hv5NyWKzfBxdeMzOzArnw5muxPVTSBfm9sM7w30k5LNbvg8/xmpmZFch7vGZmZgVy4TUzMyuQC28HJK0g6QZJL0t6XNJYSZ+qsa1vS9qr0TF2dZJC0lkV98+VNLKDbY6T1KeA2M6QtGXe/VjjOGfz5Xytn8/xViFJwOPA6Ii4PK3bEpgeEc92sG3PiFjY3v1O9r/I23RFkmYB7wAjImKWpHOBZyNiTJVtXgPWiYjZDYzD71kX55zNn/O1fr2K7rCL+TIwuyWBASJinKTPShoHDAAWAIdFxFPpU9+uwNLANEkTgVWAzwMTJE0F3omICyV9FhgNLAO8BxwcEa9Juh94CtgCuBAYU8hv2lwfAFcBRwLnVD4gaQPgYqAfMAE4DDgEGA78RdJrEbFrq202Bf43bTM9IraR9EXgfKAvMAs4ICJel3Qan3yPBgBzgQ2BmyR9HrguIv4oaSPgPGBJYApwYERMS/9Urga+CpwE3NXIF8cWiXM2f87XekWEb+3cgGOA89tY3x/om5ZHAHel5ZHAy8CgdP80YDzQp+L+d9LyHcDKaflLwNi0fD/wk2b/7gW/zu8Aw9JrtwRwLjAyPfYMsGlavgg4Pi2/BizZRlt9gJfIPl0DLJ1+DgJ6puVdgV+28x6NAa7l46NBY4Cdgd7AAxXtHdzyPqVYjmr26+ibc7ag19j5WufNe7y16QtcKGkEsJDsj7DFHRHxXsX9myJifuXGkpYEtgRuzI6MIeD9iqeMzSXqEouIqZL+SJYgAEgaQvbP8pG06krge8BPqzS1BvBapMOKETEtrV8KuDLttfQAplds0/o9ui5ShlZYHfgCcG96z3oBkyoe73bvWRfjnG0g52t9XHirew74jzbWH0f2qWk/skNXr1U8NqfVc1vfh+wP6a2IWK+dftvapjs4F7gbuC2Hts8A/hQRl0pah08eDuzsezYhIrZtp/3u+p6VjXO2OM7XGnlUc3V3A4MqR+xJ2oLsUNWU9ClrZNubti99un5L0i6pzZ7pj6tbi4i/kR1G2j3dnwF8IGnj9JR9gQfT8ixgYBvNPA+s3PJ6Slo6rR8EvJGWR9YQ3vPAipI2TO32lbRGDe1YvpyzBXG+1s6Ft4qUpF8Dvpa+mjAJOBr4H+BwSU+RDbSoxTeBoyU9TXZe5Mv1R7xYOIdsIEaLkcAv0qCXgWTnjQB+Cdwn6ebKjdMhqP2AX6XX9rr00CjgfElPAp84jNgZqd29gP9N7T5BdijLSsQ5Wzjnaw38dSIzM7MCeY/XzMysQC68ZmZmBXLhNTMzK5ALr5mZWYFceM3MzArkwlsSkhZIekrSs8quptK/jrbeST93lfTdKs9bWdKeNbT/WprJp3Vbj3ew3cg0oXpn++mwTbNmcc62G59ztgMuvOUxIyLWi4h1yL639u3KByX1XNQGI+LmiDi/ylNWBhY5ic0McM5ajVx4y2kc8DlJ20i6V9KtwHhJAySNkfSYpCckbQ8gaVh63rOS/qelkcpPq5I+LelmSU9LmpCu4nE2sF361H5omo3nvNT+05L2Tdv2l3S9pMmSxpDNU9supSvBSHpS0qOS1qt4eBVJD0p6UdJxFdvsX9Hvv8ztKmnd1N5T6bZsja+tWR6cs//apnO2Pc28QoNvn7hKxzvpZy/gJuAIYBtgJjA8PfYj4BtpeSjZtGgiuxRZy1VAjqhoayRwblq+FvhWWu5LNl/tNmQTjLfEcBhwYlruBzxNNsvPicDP0/odgaDVlUbIPok/nparXQnmr8AQskt1PQ98FliTbMaaXul5VwA7tWrzgor4+5GuTuKbb826OWeds7XefJGE8hiibDo7yOY3vRzYHBgfEVPS+q8AO0s6Od0fAHyK7DqgP0rrrgLObKP9LYG9ASLiA7I5VVs/5yvAOpL2S/cHA6um9kelbW+VNL31hq1UuxLM7ZHN6UraK9iMLKm/CDyeYupPNsVb5dVEHgJOlbQMcG1EvNJBDGZ5c846Z2viwlseM6LVlU/SH3TlVTR6ALtExOttPK8Rc3/2AA6PiAfqbP842r8STLRajtTvLyPi9Fb9rvzREyN+J+lRYBfgLkl7RMSTixCTWaM5Z52zNfE53q7lTrILfQNQcR7mz2QTgkM2kXtbxgGHpO36SBrAv14x5E7gSKVBIZLWScsftS9pB7JrZVYziPavBLODpMGp/38HHgbuAfZKn4yRtKyk5So3krQq8HJkA0/uBNbqIAazMnDOOmf/hQtv13ImMFjSREmTyc7jAJwO7CrpWeAz7Wx7LNkVWyaSHQIaDkwEercM1CC7gshrwITU1vlk56NGAyulPvciO+dTzWjavxLMY8AtwATg0oh4OSImkQ0auSfF9ydg6Vbb7QU8m9ocDvyhgxjMysA565z9F746kZmZWYG8x2tmZlYgF14zM7MCufCamZkVyIXXzMysQC68ZmZmBXLhNTMzK5ALr5mZWYFceM3MzAr0/2OkR/l9UhtLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 560x266 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 560x266 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 560x266 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 560x266 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 560x266 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrixes(cms_forest_19, len(cms_forest_19))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "O f1 médio da validação cruzada do modelo Random Forest (m=19) é de: 0.428\n"
     ]
    }
   ],
   "source": [
    "print(\"O f1 médio da validação cruzada do modelo Random Forest (m=19) é de: {0:.3f}\".format(np.mean(f1s_forest_19)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BILIRUBIN': 0.21558407073013622,\n",
       " 'ASCITES': 0.13437093895304084,\n",
       " 'ALBUMIN': 0.1286123539638832,\n",
       " 'SGOT': 0.09756150812173478,\n",
       " 'ALK_PHOSPHATE': 0.09050292293308265,\n",
       " 'AGE': 0.06948993467567469,\n",
       " 'PROTIME': 0.06074192186084553,\n",
       " 'SPIDERS': 0.051677039775679834,\n",
       " 'VARICES': 0.041415187088488306,\n",
       " 'SPLEEN_PALPABLE': 0.020381779322993253,\n",
       " 'LIVER_FIRM': 0.01865178453795199,\n",
       " 'MALAISE': 0.017423125428459845,\n",
       " 'LIVER_BIG': 0.011823674357746077,\n",
       " 'FATIGUE': 0.009491656222777655,\n",
       " 'HISTOLOGY': 0.009234422094984507,\n",
       " 'STEROID': 0.0072688665538086965,\n",
       " 'ANOREXIA': 0.006857323722954952,\n",
       " 'SEX': 0.005778193315095649,\n",
       " 'ANTIVIRALS': 0.003133296340661455}"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_overview = dict(zip(feature_keys, model_forest_19.feature_importances_))\n",
    "{k: v for k, v in sorted(features_overview.items(), key=lambda item: item[1], reverse=True)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest (m=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_forest_4 = RandomForestClassifier(n_estimators=100, max_features=4)\n",
    "cv_forest_4 = StratifiedKFold(n_splits=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 560x266 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cms_forest_4 = []\n",
    "f1s_forest_4 = []\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (8,3.8)\n",
    "plt.rcParams[\"figure.dpi\"] = (70)\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "for i, (train, test) in enumerate(cv_forest_4.split(X, y)):\n",
    "    model_forest_4.fit(X[train], y[train])\n",
    "    viz = plot_roc_curve(model_forest_4, X[test], y[test],\n",
    "                         name='ROC fold {}'.format(i+1),\n",
    "                         alpha=1, lw=1, ax=ax)\n",
    "\n",
    "    y_pred = model_forest_4.predict(X[test])\n",
    "    cms_forest_4.append(confusion_matrix(y[test], y_pred))\n",
    "    f1s_forest_4.append(f1_score(y[test], y_pred))\n",
    "\n",
    "ax.set(xlim=[-0.05, 1.05], ylim=[-0.05, 1.05],\n",
    "       title=\"Random Forest (m=4) on hepatitits data\")\n",
    "ax.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 560x266 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 560x266 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 560x266 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 560x266 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 560x266 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 560x266 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrixes(cms_forest_4, len(cms_forest_4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "O f1 médio da validação cruzada do modelo Random Forest (m=4) é de: 0.470\n"
     ]
    }
   ],
   "source": [
    "print(\"O f1 médio da validação cruzada do modelo Random Forest (m=4) é de: {0:.3f}\".format(np.mean(f1s_forest_4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BILIRUBIN': 0.15102265445594013,\n",
       " 'ALBUMIN': 0.14737321877640316,\n",
       " 'SGOT': 0.09256708515333464,\n",
       " 'ALK_PHOSPHATE': 0.08661644558154435,\n",
       " 'ASCITES': 0.07617866557781677,\n",
       " 'PROTIME': 0.07535573125698226,\n",
       " 'AGE': 0.07368401463843663,\n",
       " 'SPIDERS': 0.06822123265848996,\n",
       " 'VARICES': 0.04114059658693643,\n",
       " 'SPLEEN_PALPABLE': 0.035949953541704306,\n",
       " 'MALAISE': 0.026804244107332065,\n",
       " 'LIVER_BIG': 0.023459224446313584,\n",
       " 'HISTOLOGY': 0.0218273243561564,\n",
       " 'LIVER_FIRM': 0.021506208656520465,\n",
       " 'FATIGUE': 0.016214988488423784,\n",
       " 'ANOREXIA': 0.014204580951393802,\n",
       " 'STEROID': 0.012240283728438401,\n",
       " 'SEX': 0.009563984912070271,\n",
       " 'ANTIVIRALS': 0.006069562125762604}"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_overview = dict(zip(feature_keys, model_forest_4.feature_importances_))\n",
    "{k: v for k, v in sorted(features_overview.items(), key=lambda item: item[1], reverse=True)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comentários finais"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O melhor resultado obtido foi o modelo de Floresta Randômica com raiz quadrada de variáveis preditoras. Nesse caso, temos que as 2 principais features foram as informações de BILIRUBIN e ALBUMIN, ambas variáveis numéricas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1s_forest_4_features = {k: v for k, v in sorted(features_overview.items(), key=lambda item: item[1], reverse=True)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 560x266 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "\n",
    "ax.set_title('Feature Importances (Random Forest, m=4)', size=15)\n",
    "ax.bar(f1s_forest_4_features.keys(), f1s_forest_4_features.values())\n",
    "ax.xaxis.set_tick_params(rotation=90)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como visto nas aulas teóricas, esse é um resultado esperado, já que o uso de um número limitado de variáveis em cada árvore da floresta permite identificar combinações de features mais bem-sucedidas na predição, independentes entre si."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Também é possível observar que a árvore de decisão foi o modelo com menor acurácia, o que também já era esperado, como visto nas aulas teóricas. Isso acontece porque a floresta randômica explora diferentes árvores de decisão, com ensemble learning."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}