CAIC 2022/Oficina_DecisionTree.ipynb

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      "execution_count": null,
      "metadata": {
        "id": "eP9oV5d_xcX0"
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      "source": [
        "# importando o conjunto de dados de flores\n",
        "import seaborn as sns\n",
        "\n",
        "# salvando os dados em uma variável\n",
        "iris = sns.load_dataset('iris')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# apresentando os dados\n",
        "iris"
      ],
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          "height": 424
        },
        "id": "aDlySd5e0okR",
        "outputId": "a16e76eb-7503-44bb-a326-3854113478d2"
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      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width    species\n",
              "0             5.1          3.5           1.4          0.2     setosa\n",
              "1             4.9          3.0           1.4          0.2     setosa\n",
              "2             4.7          3.2           1.3          0.2     setosa\n",
              "3             4.6          3.1           1.5          0.2     setosa\n",
              "4             5.0          3.6           1.4          0.2     setosa\n",
              "..            ...          ...           ...          ...        ...\n",
              "145           6.7          3.0           5.2          2.3  virginica\n",
              "146           6.3          2.5           5.0          1.9  virginica\n",
              "147           6.5          3.0           5.2          2.0  virginica\n",
              "148           6.2          3.4           5.4          2.3  virginica\n",
              "149           5.9          3.0           5.1          1.8  virginica\n",
              "\n",
              "[150 rows x 5 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "# importanto a biblioteca para plotar os dados\n",
        "import matplotlib.pyplot as plt\n",
        " \n",
        "# plotando os dados\n",
        "sns.FacetGrid(iris, \n",
        "              hue =\"species\",\n",
        "              height = 6).map(plt.scatter,\n",
        "                              'sepal_width',\n",
        "                              'petal_width').add_legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "ILjCtNjfxo9-",
        "outputId": "c97ff534-8190-407f-99b8-e3870cd410cf"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f9ea9d29750>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
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          "data": {
            "text/plain": [
              "<Figure size 516.75x432 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# verificando os titulos das colunas\n",
        "iris.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-o08NKNo1XNl",
        "outputId": "84d1eaa5-caf1-480e-c61e-e06ba4523baf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width',\n",
              "       'species'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# separando as entradas \n",
        "X = iris.loc[:,['sepal_length', 'sepal_width', 'petal_length', 'petal_width']]\n",
        "\n",
        "# apresentando as entradas \n",
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "rPB5uqsq2zJK",
        "outputId": "ef895edd-a60e-4226-c94c-f1bbf773a74c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width\n",
              "0             5.1          3.5           1.4          0.2\n",
              "1             4.9          3.0           1.4          0.2\n",
              "2             4.7          3.2           1.3          0.2\n",
              "3             4.6          3.1           1.5          0.2\n",
              "4             5.0          3.6           1.4          0.2\n",
              "..            ...          ...           ...          ...\n",
              "145           6.7          3.0           5.2          2.3\n",
              "146           6.3          2.5           5.0          1.9\n",
              "147           6.5          3.0           5.2          2.0\n",
              "148           6.2          3.4           5.4          2.3\n",
              "149           5.9          3.0           5.1          1.8\n",
              "\n",
              "[150 rows x 4 columns]"
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              "      <th>sepal_length</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
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              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
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              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
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              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
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              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
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              "    <tr>\n",
              "      <th>149</th>\n",
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              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 4 columns</p>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ]
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          "metadata": {},
          "execution_count": 29
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# separando as saidas \n",
        "y = iris.loc[:,['species']]\n",
        "\n",
        "# apresentando as entradas \n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "tmkfBzny27d8",
        "outputId": "0799d28c-2080-42e2-cdef-1bc86c709024"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       species\n",
              "0       setosa\n",
              "1       setosa\n",
              "2       setosa\n",
              "3       setosa\n",
              "4       setosa\n",
              "..         ...\n",
              "145  virginica\n",
              "146  virginica\n",
              "147  virginica\n",
              "148  virginica\n",
              "149  virginica\n",
              "\n",
              "[150 rows x 1 columns]"
            ],
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              "    <tr>\n",
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              "      <td>setosa</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>setosa</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>setosa</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>virginica</td>\n",
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              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>virginica</td>\n",
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              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>virginica</td>\n",
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              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>virginica</td>\n",
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              "<p>150 rows × 1 columns</p>\n",
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              "\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# importando a biblioteca para separar os dados em treino e teste\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# dividindo o dataset em treinamento (70%) e teste (30$)%\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)"
      ],
      "metadata": {
        "id": "a_HTMw0hxgif"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# verificando o conjunto de teste\n",
        "X_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "bum0oVod3NV3",
        "outputId": "529a46a6-08fd-4a35-b73e-d94b8560b5e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width\n",
              "114           5.8          2.8           5.1          2.4\n",
              "62            6.0          2.2           4.0          1.0\n",
              "33            5.5          4.2           1.4          0.2\n",
              "107           7.3          2.9           6.3          1.8\n",
              "7             5.0          3.4           1.5          0.2\n",
              "100           6.3          3.3           6.0          2.5\n",
              "40            5.0          3.5           1.3          0.3\n",
              "86            6.7          3.1           4.7          1.5\n",
              "76            6.8          2.8           4.8          1.4\n",
              "71            6.1          2.8           4.0          1.3\n",
              "134           6.1          2.6           5.6          1.4\n",
              "51            6.4          3.2           4.5          1.5\n",
              "73            6.1          2.8           4.7          1.2\n",
              "54            6.5          2.8           4.6          1.5\n",
              "63            6.1          2.9           4.7          1.4\n",
              "37            4.9          3.6           1.4          0.1\n",
              "78            6.0          2.9           4.5          1.5\n",
              "90            5.5          2.6           4.4          1.2\n",
              "45            4.8          3.0           1.4          0.3\n",
              "16            5.4          3.9           1.3          0.4\n",
              "121           5.6          2.8           4.9          2.0\n",
              "66            5.6          3.0           4.5          1.5\n",
              "24            4.8          3.4           1.9          0.2\n",
              "8             4.4          2.9           1.4          0.2\n",
              "126           6.2          2.8           4.8          1.8\n",
              "22            4.6          3.6           1.0          0.2\n",
              "44            5.1          3.8           1.9          0.4\n",
              "97            6.2          2.9           4.3          1.3\n",
              "93            5.0          2.3           3.3          1.0\n",
              "26            5.0          3.4           1.6          0.4\n",
              "137           6.4          3.1           5.5          1.8\n",
              "84            5.4          3.0           4.5          1.5\n",
              "27            5.2          3.5           1.5          0.2\n",
              "127           6.1          3.0           4.9          1.8\n",
              "132           6.4          2.8           5.6          2.2\n",
              "59            5.2          2.7           3.9          1.4\n",
              "18            5.7          3.8           1.7          0.3\n",
              "83            6.0          2.7           5.1          1.6\n",
              "61            5.9          3.0           4.2          1.5\n",
              "92            5.8          2.6           4.0          1.2\n",
              "112           6.8          3.0           5.5          2.1\n",
              "2             4.7          3.2           1.3          0.2\n",
              "141           6.9          3.1           5.1          2.3\n",
              "43            5.0          3.5           1.6          0.6\n",
              "10            5.4          3.7           1.5          0.2"
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              "\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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>5.8</td>\n",
              "      <td>2.8</td>\n",
              "      <td>5.1</td>\n",
              "      <td>2.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>6.0</td>\n",
              "      <td>2.2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>5.5</td>\n",
              "      <td>4.2</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>7.3</td>\n",
              "      <td>2.9</td>\n",
              "      <td>6.3</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>6.3</td>\n",
              "      <td>3.3</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.1</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>6.8</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>6.1</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>6.1</td>\n",
              "      <td>2.6</td>\n",
              "      <td>5.6</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>6.4</td>\n",
              "      <td>3.2</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>6.1</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>6.5</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>6.1</td>\n",
              "      <td>2.9</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>6.0</td>\n",
              "      <td>2.9</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>5.5</td>\n",
              "      <td>2.6</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>4.8</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>5.6</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.9</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>5.6</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>4.8</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>4.4</td>\n",
              "      <td>2.9</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>6.2</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>6.2</td>\n",
              "      <td>2.9</td>\n",
              "      <td>4.3</td>\n",
              "      <td>1.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>5.0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>3.3</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>6.4</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.5</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>5.2</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>6.1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>6.4</td>\n",
              "      <td>2.8</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>5.2</td>\n",
              "      <td>2.7</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>5.7</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>6.0</td>\n",
              "      <td>2.7</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.2</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>5.8</td>\n",
              "      <td>2.6</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>6.8</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.5</td>\n",
              "      <td>2.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>6.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d8361cbc-fed9-4927-bc9c-2ac9ffeccf73')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-d8361cbc-fed9-4927-bc9c-2ac9ffeccf73 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d8361cbc-fed9-4927-bc9c-2ac9ffeccf73');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# importando a biblioteca da árvore de decisão\n",
        "from sklearn import tree\n",
        "\n",
        "# instanciando a arvore de decisao\n",
        "arvore = tree.DecisionTreeClassifier()\n",
        "\n",
        "# pegando os parametros de treinamento\n",
        "arvore.get_params(deep=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MuC9iO8bxmMP",
        "outputId": "4a8d4022-ed58-4916-feb9-38a142f8f780"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'ccp_alpha': 0.0,\n",
              " 'class_weight': None,\n",
              " 'criterion': 'gini',\n",
              " 'max_depth': None,\n",
              " 'max_features': None,\n",
              " 'max_leaf_nodes': None,\n",
              " 'min_impurity_decrease': 0.0,\n",
              " 'min_samples_leaf': 1,\n",
              " 'min_samples_split': 2,\n",
              " 'min_weight_fraction_leaf': 0.0,\n",
              " 'random_state': None,\n",
              " 'splitter': 'best'}"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# treinando a arvore de decisao\n",
        "arvore.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6InqUDUi-zNB",
        "outputId": "bac4dd85-128d-4fee-ca4d-50fbedd7da5a"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# avaliando a classificação da arvore para o conjunto de teste\n",
        "y_prev_test = arvore.predict(X_test)\n",
        "\n",
        "# avaliando a classificacao\n",
        "y_prev_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VP-qDHRYxmxx",
        "outputId": "16193339-6d27-4f09-84b4-ceda01d131a6"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['virginica', 'versicolor', 'setosa', 'virginica', 'setosa',\n",
              "       'virginica', 'setosa', 'versicolor', 'versicolor', 'versicolor',\n",
              "       'virginica', 'versicolor', 'versicolor', 'versicolor',\n",
              "       'versicolor', 'setosa', 'versicolor', 'versicolor', 'setosa',\n",
              "       'setosa', 'virginica', 'versicolor', 'setosa', 'setosa',\n",
              "       'virginica', 'setosa', 'setosa', 'versicolor', 'versicolor',\n",
              "       'setosa', 'virginica', 'versicolor', 'setosa', 'virginica',\n",
              "       'virginica', 'versicolor', 'setosa', 'virginica', 'versicolor',\n",
              "       'versicolor', 'virginica', 'setosa', 'virginica', 'setosa',\n",
              "       'setosa'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# importando a metrica de avaliação de desempenho\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# avaliando a taxa de acerto da arvore\n",
        "accuracy_score(y_test, y_prev_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I1zqB5jh4IJL",
        "outputId": "9b800564-a29f-44f7-997b-c7063514f8fa"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9777777777777777"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# como plotar a fronteira de decisão\n",
        "#https://scikit-learn.org/stable/auto_examples/tree/plot_iris_dtc.html#sphx-glr-auto-examples-tree-plot-iris-dtc-py"
      ],
      "metadata": {
        "id": "KtAkenAg4QIb"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}