Decision_tree.ipynb

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        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
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        "df=pd.read_excel(\"/content/decision_tree.xlsx\")\n",
        "df"
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              "      Days outlook temperature  routine wearjacket\n",
              "0        1   sunny        cold   indoor         no\n",
              "1        2   sunny        warm  outdoor         no\n",
              "2        3  cloudy        warm   indoor         no\n",
              "3        4   sunny        warm   indoor         no\n",
              "4        5  cloudy        cold   indoor        yes\n",
              "5        6  cloudy        cold  outdoor        yes\n",
              "6        7   sunny        cold  outdoor        yes"
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        "y=df['wearjacket']"
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              "0     no\n",
              "1     no\n",
              "2     no\n",
              "3     no\n",
              "4    yes\n",
              "Name: wearjacket, dtype: object"
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              "  outlook temperature  routine wearjacket\n",
              "0   sunny        cold   indoor         no\n",
              "1   sunny        warm  outdoor         no\n",
              "2  cloudy        warm   indoor         no\n",
              "3   sunny        warm   indoor         no\n",
              "4  cloudy        cold   indoor        yes"
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              "  outlook temperature  routine\n",
              "0   sunny        cold   indoor\n",
              "1   sunny        warm  outdoor\n",
              "2  cloudy        warm   indoor\n",
              "3   sunny        warm   indoor\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6e64753f-7193-4fc2-a97a-d5dcb133ddc6 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-6e64753f-7193-4fc2-a97a-d5dcb133ddc6');\n",
              "          const dataTable =\n",
              "            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",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Decision tree"
      ],
      "metadata": {
        "id": "0O6f7LtJb_VR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "dc=DecisionTreeClassifier()"
      ],
      "metadata": {
        "id": "qtq5-LoCb5BN"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#preprocessing lavel encoding"
      ],
      "metadata": {
        "id": "oB6Q1HTec1Su"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le=LabelEncoder()"
      ],
      "metadata": {
        "id": "Wk98l0iqcVwa"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.outlook=le.fit_transform(df['outlook'])"
      ],
      "metadata": {
        "id": "Mx_5SIDAdMqP"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "R_Qq19uUdWP3",
        "outputId": "41e0849e-39cb-4e65-f9e1-bbc4d7730071"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   outlook temperature  routine\n",
              "0        1        cold   indoor\n",
              "1        1        warm  outdoor\n",
              "2        0        warm   indoor\n",
              "3        1        warm   indoor\n",
              "4        0        cold   indoor"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6c6fcc66-ae01-4c3b-8a0b-e620df9dcde6\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>outlook</th>\n",
              "      <th>temperature</th>\n",
              "      <th>routine</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>cold</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>warm</td>\n",
              "      <td>outdoor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>warm</td>\n",
              "      <td>indoor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>warm</td>\n",
              "      <td>indoor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>cold</td>\n",
              "      <td>indoor</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
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              "          const dataTable =\n",
              "            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",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x.temperature=le.fit_transform(df['temperature'])\n",
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "BShC_ExJdfz0",
        "outputId": "fdbe6144-fc08-4f29-b6db-57ed1d4e22de"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   outlook  temperature  routine\n",
              "0        1            0   indoor\n",
              "1        1            1  outdoor\n",
              "2        0            1   indoor\n",
              "3        1            1   indoor\n",
              "4        0            0   indoor"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-177e133f-2df5-4b54-a69f-761cd8999449\">\n",
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              "</style>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>outlook</th>\n",
              "      <th>temperature</th>\n",
              "      <th>routine</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>outdoor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>indoor</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "    <tr>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>indoor</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-177e133f-2df5-4b54-a69f-761cd8999449')\"\n",
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              "\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-177e133f-2df5-4b54-a69f-761cd8999449 button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-177e133f-2df5-4b54-a69f-761cd8999449');\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",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x.routine=le.fit_transform(df['routine'])\n",
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "jItlxYOae0rc",
        "outputId": "7bcb1a90-0ec2-4d99-bb33-77d551939088"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   outlook  temperature  routine\n",
              "0        1            0        0\n",
              "1        1            1        1\n",
              "2        0            1        0\n",
              "3        1            1        0\n",
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              "      <th></th>\n",
              "      <th>outlook</th>\n",
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              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5d2fce2a-e03b-4dfe-945e-5b4eee00a19b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5d2fce2a-e03b-4dfe-945e-5b4eee00a19b 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-5d2fce2a-e03b-4dfe-945e-5b4eee00a19b');\n",
              "          const dataTable =\n",
              "            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",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dc.fit(x,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-xNR-q1rgIAC",
        "outputId": "d78dd6a2-fddf-4aa0-a634-bddfbb013f04"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dc.predict([[1,1,0]]) # 1=sunny 1=warm 0 =indoor"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KiVqNK7FgnPl",
        "outputId": "aaa410b8-2411-4d31-ef7e-c209bbe14653"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['no'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dc.predict([[1,0,1]]) # 1=sunny 0=warm 1=outdoor"
      ],
      "metadata": {
        "id": "YbPLIJG8i9Dw",
        "outputId": "39c87cde-f571-4825-c825-ac32885d3d96",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['yes'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0BGeEFCcjz04"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import tree\n",
        "tree.plot_tree(dc,filled=True,rounded=True,feature_names=x.columns)"
      ],
      "metadata": {
        "id": "lMXBCz9fj0Ic",
        "outputId": "da4f679f-9e71-4a33-9dc1-6b6d4d7edc15",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        }
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.6666666666666666, 0.875, 'temperature <= 0.5\\ngini = 0.49\\nsamples = 7\\nvalue = [4, 3]'),\n",
              " Text(0.5, 0.625, 'routine <= 0.5\\ngini = 0.375\\nsamples = 4\\nvalue = [1, 3]'),\n",
              " Text(0.3333333333333333, 0.375, 'outlook <= 0.5\\ngini = 0.5\\nsamples = 2\\nvalue = [1, 1]'),\n",
              " Text(0.16666666666666666, 0.125, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
              " Text(0.5, 0.125, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
              " Text(0.6666666666666666, 0.375, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
              " Text(0.8333333333333334, 0.625, 'gini = 0.0\\nsamples = 3\\nvalue = [3, 0]')]"
            ]
          },
          "metadata": {},
          "execution_count": 93
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fWr6T-TRj96H"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}