Traditional-CorrelatedCleveland-Heart_Disease_Classification.ipynb

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      "source": [
        "# Heart Disease Classification using Cleveland Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This notebook applies traditional machine learning approach to Cleveland Heart dataset for classifying heart disease.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "**The steps performed on the notebook is as follows:** The steps include reducing the number of features (columns) based on correlation of the feature with the label column.\n",
        "\n",
        "1) To clean the data, remove the rows that have missing information on columns ca and thal.\n",
        "\n",
        "2) Converted all fields to numeric data types\n",
        "\n",
        "3) Analyzed the **correlations** between the fields and the label and then dropped 4 fields ('chol', 'fbs', 'trestbps', 'restecg’) that has correlation less than 0.2\n",
        "\n",
        "4) Scaled the data\n",
        "\n",
        "5) Calculated cross validation (k=5) accuracy scores for 10 different machine learning algorithms:\n",
        "\n",
        "- Stochastic Gradient Descent (SGD)\n",
        "- LogisticRegression\n",
        "- Support Vector Machine (kernel='linear' parameter)\n",
        "- Support Vector Machine (kernel = 'rbf' parameter)\n",
        "- DecisionTreeClassifier\n",
        "- RandomForestClassifier\n",
        "- ExtraTreesClassifier\n",
        "- AdaBoostClassifier\n",
        "- GradientBoostingClassifier\n",
        "- XGBoost\n",
        "\n",
        "\n",
        "6) Identified top-performing algorithms (adaboost, random forest, gradient boosting, XGB) that can be fine-tuned by adjusting hyperparameters. Improved accuracy scores by performing Grid Search with the following hyperparameters:\n",
        "\n",
        "- n_estimators = [100, 200, 300, 400, 500]\n",
        "- learning_rate = [0.3, 0.1, 0.05]\n",
        "- max_features = [1, 0.7, 0.5, 0.4, 0.3]\n",
        "- subsample = [1, 0.5, 0.3]\n",
        "- max_samples = [1, 0.5, 0.3, 0.2]\n",
        "- bootstrap = [True,False]\n",
        "\n",
        "7) Using the fine-tuned estimators (adaboost, random forest, gradient boosting, XGB) and top-performing other estimators (SVC, SGD, Logistic regression), formed an ensemble voting classifier.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
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    {
      "cell_type": "markdown",
      "source": [
        "#### Import the data and perform exploratory data analysis:"
      ],
      "metadata": {
        "id": "7kZQMn5eIAc7"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "a5Eb5SNhz_l6"
      },
      "outputs": [],
      "source": [
        "from google.colab import files\n",
        "import pandas as pd\n",
        "import io\n",
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "Ipoqlpnxz_l8"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "train_csv_path = \"processed.cleveland.data\"\n",
        "#Upload the file to the files section of colab before running the code\n",
        "\n",
        "train_data = pd.read_csv(train_csv_path, sep = '\\t')"
      ]
    },
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        "outputId": "c3c31525-1512-46c7-806d-cdfa69076e1d"
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              "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
              "0   63    1   1       145   233    1        2      150      0      2.3      3   \n",
              "1   67    1   4       160   286    0        2      108      1      1.5      2   \n",
              "2   67    1   4       120   229    0        2      129      1      2.6      2   \n",
              "3   37    1   3       130   250    0        0      187      0      3.5      3   \n",
              "4   41    0   2       130   204    0        2      172      0      1.4      1   \n",
              "\n",
              "  ca thal  num  \n",
              "0  0    6    0  \n",
              "1  3    3    2  \n",
              "2  2    7    1  \n",
              "3  0    3    0  \n",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 303 entries, 0 to 302\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       303 non-null    int64  \n",
            " 1   sex       303 non-null    int64  \n",
            " 2   cp        303 non-null    int64  \n",
            " 3   trestbps  303 non-null    int64  \n",
            " 4   chol      303 non-null    int64  \n",
            " 5   fbs       303 non-null    int64  \n",
            " 6   restecg   303 non-null    int64  \n",
            " 7   thalach   303 non-null    int64  \n",
            " 8   exang     303 non-null    int64  \n",
            " 9   oldpeak   303 non-null    float64\n",
            " 10  slope     303 non-null    int64  \n",
            " 11  ca        303 non-null    object \n",
            " 12  thal      303 non-null    object \n",
            " 13  num       303 non-null    int64  \n",
            "dtypes: float64(1), int64(11), object(2)\n",
            "memory usage: 33.3+ KB\n"
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              "              age         sex          cp    trestbps        chol         fbs  \\\n",
              "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
              "mean    54.438944    0.679868    3.158416  131.689769  246.693069    0.148515   \n",
              "std      9.038662    0.467299    0.960126   17.599748   51.776918    0.356198   \n",
              "min     29.000000    0.000000    1.000000   94.000000  126.000000    0.000000   \n",
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              "max     77.000000    1.000000    4.000000  200.000000  564.000000    1.000000   \n",
              "\n",
              "          restecg     thalach       exang     oldpeak       slope         num  \n",
              "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000  \n",
              "mean     0.990099  149.607261    0.326733    1.039604    1.600660    0.937294  \n",
              "std      0.994971   22.875003    0.469794    1.161075    0.616226    1.228536  \n",
              "min      0.000000   71.000000    0.000000    0.000000    1.000000    0.000000  \n",
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              "75%      2.000000  166.000000    1.000000    1.600000    2.000000    2.000000  \n",
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              "      <th>25%</th>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>211.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <th>50%</th>\n",
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              "      <td>1.000000</td>\n",
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          "metadata": {},
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      ],
      "source": [
        "train_data.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Identify non-numeric data in the object fields (thal and ca)"
      ],
      "metadata": {
        "id": "CJ4nJaosR7vw"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "WojYLTpZjQPC",
        "outputId": "54056fc3-bc5b-4d94-874d-c30cf11fca5e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      0.0\n",
              "1      3.0\n",
              "2      2.0\n",
              "3      0.0\n",
              "4      0.0\n",
              "      ... \n",
              "298    0.0\n",
              "299    2.0\n",
              "300    1.0\n",
              "301    1.0\n",
              "302    NaN\n",
              "Name: ca, Length: 303, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "ca = pd.to_numeric(train_data.ca, errors='coerce') #Convert to numeric using 'coerce' which fills bad values with 'nan'\n",
        "ca"
      ]
    },
    {
      "cell_type": "code",
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        "id": "jJWU_9TFji37",
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              "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
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              "302   38    1   3       138   175    0        0      173      0      0.0   \n",
              "\n",
              "     slope ca thal  num  \n",
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      "source": [
        "train_data[ca.isna()] # Use isna to return a boolean index. Then apply that index to the data frame"
      ]
    },
    {
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      "execution_count": 8,
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              "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
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              "266   52    1   4       128   204    1        0      156      1      1.0   \n",
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              "     slope ca thal  num  \n",
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              "    .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-733dcd31-5f9f-4017-b453-83c36b26dcfe 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-733dcd31-5f9f-4017-b453-83c36b26dcfe');\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": 8
        }
      ],
      "source": [
        "thal = pd.to_numeric(train_data.thal, errors='coerce') #Convert to numeric using 'coerce' which fills bad values with 'nan'\n",
        "idx = thal.isna()\n",
        "train_data[idx] # Use isna to return a boolean index. Then apply that index to the data frame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XgCle_eopQB7",
        "outputId": "5871fc26-529b-41bd-c4bf-7697db7a3c5d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         303\n",
              "sex         303\n",
              "cp          303\n",
              "trestbps    303\n",
              "chol        303\n",
              "fbs         303\n",
              "restecg     303\n",
              "thalach     303\n",
              "exang       303\n",
              "oldpeak     303\n",
              "slope       303\n",
              "ca          303\n",
              "thal        303\n",
              "num         303\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "train_data.count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fddsONkboOe2",
        "outputId": "56d4432d-6087-492c-e95c-09ab2c1bd623"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         301\n",
              "sex         301\n",
              "cp          301\n",
              "trestbps    301\n",
              "chol        301\n",
              "fbs         301\n",
              "restecg     301\n",
              "thalach     301\n",
              "exang       301\n",
              "oldpeak     301\n",
              "slope       301\n",
              "ca          301\n",
              "thal        301\n",
              "num         301\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "update_df = train_data[(train_data.thal != '?')]\n",
        "update_df.count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CAsl7CK-ptjI",
        "outputId": "00d264d6-de63-4f63-afd0-1f6bad8bb8a3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         297\n",
              "sex         297\n",
              "cp          297\n",
              "trestbps    297\n",
              "chol        297\n",
              "fbs         297\n",
              "restecg     297\n",
              "thalach     297\n",
              "exang       297\n",
              "oldpeak     297\n",
              "slope       297\n",
              "ca          297\n",
              "thal        297\n",
              "num         297\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "update_df = update_df[(update_df.ca != '?')]\n",
        "update_df.count()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### After cleaning the non-mumeric data, convert the fields to int64 type"
      ],
      "metadata": {
        "id": "9nAxg3mfSGly"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "VDPfLcJApqWD"
      },
      "outputs": [],
      "source": [
        "update_df.ca = update_df.ca.astype('int64')\n",
        "update_df.thal = update_df.thal.astype('int64')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wrmFaWgUp1b2",
        "outputId": "af39c208-d7ee-4e6d-ce9b-5c43db1f8306"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 297 entries, 0 to 301\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       297 non-null    int64  \n",
            " 1   sex       297 non-null    int64  \n",
            " 2   cp        297 non-null    int64  \n",
            " 3   trestbps  297 non-null    int64  \n",
            " 4   chol      297 non-null    int64  \n",
            " 5   fbs       297 non-null    int64  \n",
            " 6   restecg   297 non-null    int64  \n",
            " 7   thalach   297 non-null    int64  \n",
            " 8   exang     297 non-null    int64  \n",
            " 9   oldpeak   297 non-null    float64\n",
            " 10  slope     297 non-null    int64  \n",
            " 11  ca        297 non-null    int64  \n",
            " 12  thal      297 non-null    int64  \n",
            " 13  num       297 non-null    int64  \n",
            "dtypes: float64(1), int64(13)\n",
            "memory usage: 34.8 KB\n"
          ]
        }
      ],
      "source": [
        "update_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "oEvgtYKbr3ZC"
      },
      "outputs": [],
      "source": [
        "train_data = update_df"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Check correlation to remove not-so-correlated columns"
      ],
      "metadata": {
        "id": "RL7zpG9BSPq5"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JBoyga8ZeRWv",
        "outputId": "8f4b0d81-8ab1-43e5-da42-cd6b423a5c17"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "num         1.000000\n",
              "ca          0.521178\n",
              "thal        0.513377\n",
              "oldpeak     0.501461\n",
              "cp          0.404248\n",
              "exang       0.391613\n",
              "slope       0.374689\n",
              "sex         0.226797\n",
              "age         0.222156\n",
              "restecg     0.184136\n",
              "trestbps    0.159620\n",
              "chol        0.066448\n",
              "fbs         0.049040\n",
              "thalach    -0.420639\n",
              "Name: num, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "corr_matrix = train_data.corr()\n",
        "corr_matrix[\"num\"].sort_values(ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "N10rU2b_dN67"
      },
      "outputs": [],
      "source": [
        "# drop the unimportant colums and leave at least 10 columns besides 'Survivided'\n",
        "drop_columns = ['chol', 'fbs', 'trestbps', 'restecg']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3qQf0qnY_EfQ",
        "outputId": "04ad50b3-d23e-49ad-8926-c29643e9bb6f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 297 entries, 0 to 301\n",
            "Data columns (total 10 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   age      297 non-null    int64  \n",
            " 1   sex      297 non-null    int64  \n",
            " 2   cp       297 non-null    int64  \n",
            " 3   thalach  297 non-null    int64  \n",
            " 4   exang    297 non-null    int64  \n",
            " 5   oldpeak  297 non-null    float64\n",
            " 6   slope    297 non-null    int64  \n",
            " 7   ca       297 non-null    int64  \n",
            " 8   thal     297 non-null    int64  \n",
            " 9   num      297 non-null    int64  \n",
            "dtypes: float64(1), int64(9)\n",
            "memory usage: 25.5 KB\n"
          ]
        }
      ],
      "source": [
        "important_data = train_data.drop(columns = drop_columns)\n",
        "important_data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ELxAuqnU_Rpf",
        "outputId": "bb27dcd9-b9dc-4a7d-8980-2095f6067604"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "num        1.000000\n",
              "ca         0.521178\n",
              "thal       0.513377\n",
              "oldpeak    0.501461\n",
              "cp         0.404248\n",
              "exang      0.391613\n",
              "slope      0.374689\n",
              "sex        0.226797\n",
              "age        0.222156\n",
              "thalach   -0.420639\n",
              "Name: num, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "corr_matrix_imp = important_data.corr()\n",
        "corr_matrix_imp[\"num\"].sort_values(ascending=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Partition data as X and Y"
      ],
      "metadata": {
        "id": "4h23onguS398"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = important_data.drop(columns = ['num'])\n",
        "Y_train = important_data['num']"
      ],
      "metadata": {
        "id": "ml6tk1dVS3TJ"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Scale all columns"
      ],
      "metadata": {
        "id": "SM3UCGuzSh0T"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "3TQfVeo0tFvM"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "X_train = pd.DataFrame(data = StandardScaler().fit_transform( X ),\n",
        "                       index = X.index, columns= X.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "ygtdKh3yNhXy",
        "outputId": "40a7a1fa-0c75-4466-db5a-12742166e40c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                age           sex            cp       thalach         exang  \\\n",
              "count  2.970000e+02  2.970000e+02  2.970000e+02  2.970000e+02  2.970000e+02   \n",
              "mean  -1.226105e-16 -7.177199e-17 -4.037175e-17  4.784800e-16 -3.289550e-17   \n",
              "std    1.001688e+00  1.001688e+00  1.001688e+00  1.001688e+00  1.001688e+00   \n",
              "min   -2.827176e+00 -1.446980e+00 -2.240629e+00 -3.431849e+00 -6.964194e-01   \n",
              "25%   -7.241238e-01 -1.446980e+00 -1.642895e-01 -7.247694e-01 -6.964194e-01   \n",
              "50%    1.613719e-01  6.910947e-01 -1.642895e-01  1.484822e-01 -6.964194e-01   \n",
              "75%    7.148067e-01  6.910947e-01  8.738802e-01  7.160957e-01  1.435916e+00   \n",
              "max    2.485798e+00  6.910947e-01  8.738802e-01  2.287949e+00  1.435916e+00   \n",
              "\n",
              "          oldpeak         slope            ca          thal  \n",
              "count  297.000000  2.970000e+02  2.970000e+02  2.970000e+02  \n",
              "mean     0.000000 -1.136390e-16 -7.775299e-17 -1.196200e-16  \n",
              "std      1.001688  1.001688e+00  1.001688e+00  1.001688e+00  \n",
              "min     -0.906712 -9.765832e-01 -7.219761e-01 -8.942201e-01  \n",
              "25%     -0.906712 -9.765832e-01 -7.219761e-01 -8.942201e-01  \n",
              "50%     -0.219520  6.437811e-01 -7.219761e-01 -8.942201e-01  \n",
              "75%      0.467672  6.437811e-01  3.448244e-01  1.172577e+00  \n",
              "max      4.419026  2.264145e+00  2.478425e+00  1.172577e+00  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8f620ed9-a661-4672-8707-bef0ca688040\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2.970000e+02</td>\n",
              "      <td>2.970000e+02</td>\n",
              "      <td>2.970000e+02</td>\n",
              "      <td>2.970000e+02</td>\n",
              "      <td>2.970000e+02</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>2.970000e+02</td>\n",
              "      <td>2.970000e+02</td>\n",
              "      <td>2.970000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-1.226105e-16</td>\n",
              "      <td>-7.177199e-17</td>\n",
              "      <td>-4.037175e-17</td>\n",
              "      <td>4.784800e-16</td>\n",
              "      <td>-3.289550e-17</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.136390e-16</td>\n",
              "      <td>-7.775299e-17</td>\n",
              "      <td>-1.196200e-16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.001688e+00</td>\n",
              "      <td>1.001688e+00</td>\n",
              "      <td>1.001688e+00</td>\n",
              "      <td>1.001688e+00</td>\n",
              "      <td>1.001688e+00</td>\n",
              "      <td>1.001688</td>\n",
              "      <td>1.001688e+00</td>\n",
              "      <td>1.001688e+00</td>\n",
              "      <td>1.001688e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-2.827176e+00</td>\n",
              "      <td>-1.446980e+00</td>\n",
              "      <td>-2.240629e+00</td>\n",
              "      <td>-3.431849e+00</td>\n",
              "      <td>-6.964194e-01</td>\n",
              "      <td>-0.906712</td>\n",
              "      <td>-9.765832e-01</td>\n",
              "      <td>-7.219761e-01</td>\n",
              "      <td>-8.942201e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-7.241238e-01</td>\n",
              "      <td>-1.446980e+00</td>\n",
              "      <td>-1.642895e-01</td>\n",
              "      <td>-7.247694e-01</td>\n",
              "      <td>-6.964194e-01</td>\n",
              "      <td>-0.906712</td>\n",
              "      <td>-9.765832e-01</td>\n",
              "      <td>-7.219761e-01</td>\n",
              "      <td>-8.942201e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.613719e-01</td>\n",
              "      <td>6.910947e-01</td>\n",
              "      <td>-1.642895e-01</td>\n",
              "      <td>1.484822e-01</td>\n",
              "      <td>-6.964194e-01</td>\n",
              "      <td>-0.219520</td>\n",
              "      <td>6.437811e-01</td>\n",
              "      <td>-7.219761e-01</td>\n",
              "      <td>-8.942201e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.148067e-01</td>\n",
              "      <td>6.910947e-01</td>\n",
              "      <td>8.738802e-01</td>\n",
              "      <td>7.160957e-01</td>\n",
              "      <td>1.435916e+00</td>\n",
              "      <td>0.467672</td>\n",
              "      <td>6.437811e-01</td>\n",
              "      <td>3.448244e-01</td>\n",
              "      <td>1.172577e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.485798e+00</td>\n",
              "      <td>6.910947e-01</td>\n",
              "      <td>8.738802e-01</td>\n",
              "      <td>2.287949e+00</td>\n",
              "      <td>1.435916e+00</td>\n",
              "      <td>4.419026</td>\n",
              "      <td>2.264145e+00</td>\n",
              "      <td>2.478425e+00</td>\n",
              "      <td>1.172577e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8f620ed9-a661-4672-8707-bef0ca688040')\"\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",
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              "  </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-8f620ed9-a661-4672-8707-bef0ca688040 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-8f620ed9-a661-4672-8707-bef0ca688040');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "X_train.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hSGEMPMusgVk",
        "outputId": "389785a3-f296-4459-e86b-d55a48a0e320"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 297 entries, 0 to 301\n",
            "Data columns (total 9 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   age      297 non-null    float64\n",
            " 1   sex      297 non-null    float64\n",
            " 2   cp       297 non-null    float64\n",
            " 3   thalach  297 non-null    float64\n",
            " 4   exang    297 non-null    float64\n",
            " 5   oldpeak  297 non-null    float64\n",
            " 6   slope    297 non-null    float64\n",
            " 7   ca       297 non-null    float64\n",
            " 8   thal     297 non-null    float64\n",
            "dtypes: float64(9)\n",
            "memory usage: 23.2 KB\n"
          ]
        }
      ],
      "source": [
        "X_train.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ozzPXGvnuVQO"
      },
      "source": [
        "### For each of the algorithms, use Scikit-Learn's cross-validation feature to measure the performance and print out mean and std for each algoritm:\n",
        "\n",
        "1. Stochastic Gradient Descent (SGD)\n",
        "2. LogisticRegression\n",
        "3. Support Vector Machine (kernel='linear' parameter)\n",
        "4. Support Vector Machine (kernel = 'rbf' parameter)\n",
        "5. DecisionTreeClassifier\n",
        "6. RandomForestClassifier\n",
        "7. ExtraTreesClassifier\n",
        "8. AdaBoostClassifier\n",
        "9. GradientBoostingClassifier\n",
        "10. XGBoost\n",
        "-- You can import XGBoost library as follows:  \"from xgboost import XGBClassifier\"\n",
        "\n",
        "Parameters:\n",
        "- Set the scoring parameter of cross-validation to 'accuracy'\n",
        "- Use the default cv = 5 parameter of cross-validation\n",
        "- Use random_state = 42 for initializing model algorithms\n",
        "- Use the kernel parameter indicated above for SVM algorithms\n",
        "- For others, use the default parameters (no need to specify explicitly)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bcdX8i3buO7Z",
        "outputId": "a0c5a38c-9758-4621-961c-366baa15b7ba"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 2, 1, 0, 0, 0, 3, 0, 2, 1, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
              "       1, 3, 4, 0, 0, 0, 0, 3, 0, 2, 1, 0, 0, 0, 3, 1, 3, 0, 4, 0, 0, 0,\n",
              "       1, 4, 0, 4, 0, 0, 0, 0, 2, 0, 1, 1, 1, 1, 0, 0, 2, 0, 1, 0, 2, 2,\n",
              "       1, 0, 2, 1, 0, 3, 1, 1, 1, 0, 1, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0,\n",
              "       0, 0, 3, 0, 0, 0, 1, 2, 3, 0, 0, 0, 0, 0, 0, 3, 0, 2, 1, 2, 3, 1,\n",
              "       1, 0, 2, 2, 0, 0, 0, 3, 2, 3, 4, 0, 3, 1, 0, 3, 3, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 4, 3, 1, 0, 0, 1, 0, 1, 0, 1, 4, 0, 0, 0, 0, 0, 0, 4, 3,\n",
              "       1, 1, 1, 2, 0, 0, 4, 0, 0, 0, 0, 0, 1, 0, 3, 0, 1, 0, 4, 1, 0, 1,\n",
              "       0, 0, 3, 2, 0, 0, 1, 0, 0, 2, 1, 2, 0, 3, 2, 0, 3, 0, 0, 0, 1, 0,\n",
              "       0, 0, 0, 0, 3, 3, 3, 0, 1, 0, 4, 0, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       3, 1, 0, 0, 0, 3, 2, 0, 2, 1, 0, 0, 3, 2, 1, 0, 0, 0, 0, 0, 2, 0,\n",
              "       2, 2, 1, 3, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 0, 0, 4, 2, 2,\n",
              "       1, 0, 1, 0, 2, 0, 1, 0, 0, 0, 1, 0, 2, 0, 3, 0, 2, 4, 2, 0, 0, 1,\n",
              "       0, 2, 2, 1, 0, 3, 1, 1, 2, 3, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "from sklearn import preprocessing\n",
        "from sklearn import utils\n",
        "\n",
        "#convert y values to categorical values\n",
        "label_encoder = preprocessing.LabelEncoder()\n",
        "Y_train = label_encoder.fit_transform(Y_train)\n",
        "Y_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PpEU_sKXuLdL",
        "outputId": "5442b192-d137-46f9-a864-7cfb79e17c43"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 297 entries, 0 to 301\n",
            "Data columns (total 9 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   age      297 non-null    float64\n",
            " 1   sex      297 non-null    float64\n",
            " 2   cp       297 non-null    float64\n",
            " 3   thalach  297 non-null    float64\n",
            " 4   exang    297 non-null    float64\n",
            " 5   oldpeak  297 non-null    float64\n",
            " 6   slope    297 non-null    float64\n",
            " 7   ca       297 non-null    float64\n",
            " 8   thal     297 non-null    float64\n",
            "dtypes: float64(9)\n",
            "memory usage: 23.2 KB\n"
          ]
        }
      ],
      "source": [
        "X_train.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "RfTMfTvZulhK"
      },
      "outputs": [],
      "source": [
        "random_seed = 42\n",
        "scoring_parameter = \"accuracy\"\n",
        "k_fold = 5\n",
        "from sklearn.model_selection import cross_val_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wam6an3CX_E5",
        "outputId": "989f196a-10f1-4d30-cd98-285c95a059e0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SGD_Classifier: \n",
            "- mean():  0.5854237288135592\n",
            "- std():  0.06148221856951664\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import SGDClassifier\n",
        "SGD_classifier = SGDClassifier(random_state = random_seed)\n",
        "\n",
        "SGD_classifier_scores = cross_val_score(SGD_classifier, X_train, Y_train, scoring=scoring_parameter)\n",
        "\n",
        "print(\"SGD_Classifier: \")\n",
        "print(\"- mean(): \" , SGD_classifier_scores.mean())\n",
        "print(\"- std(): \" , SGD_classifier_scores.std())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jK9hoUVgYGvB",
        "outputId": "7010b039-301e-48b8-8b2f-f326d11ca13e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic_Regression: \n",
            "- mean():  0.5854802259887004\n",
            "- std():  0.046590682452654957\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "logistic_regression = LogisticRegression(random_state = random_seed)\n",
        "\n",
        "logistic_regression_scores = cross_val_score(logistic_regression, X_train, Y_train, scoring=scoring_parameter, cv=k_fold)\n",
        "\n",
        "print(\"Logistic_Regression: \")\n",
        "print(\"- mean(): \" , logistic_regression_scores.mean())\n",
        "print(\"- std(): \" , logistic_regression_scores.std())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6y0o-aqoYRZc",
        "outputId": "dd8e6663-8027-4744-d7ea-d5e73c220cf2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVC kernel = linear: \n",
            "- mean():  0.5486440677966101\n",
            "- std():  0.025378742370995574\n"
          ]
        }
      ],
      "source": [
        "from sklearn.svm import SVC\n",
        "SVC_linear = SVC(random_state = random_seed, kernel = 'linear')\n",
        "\n",
        "SVC_linear_scores = cross_val_score(SVC_linear,\n",
        "                                    X_train,\n",
        "                                    Y_train,\n",
        "                                    scoring=scoring_parameter,\n",
        "                                    cv=k_fold)\n",
        "\n",
        "print(\"SVC kernel = linear: \")\n",
        "print(\"- mean(): \" , SVC_linear_scores.mean())\n",
        "print(\"- std(): \" , SVC_linear_scores.std())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FdKgKNGHYX7T",
        "outputId": "5d198608-edc2-4baa-ef5a-7dcf424b2d8f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVC kernel = rbf: \n",
            "- mean():  0.5689830508474577\n",
            "- std():  0.02344898221988851\n"
          ]
        }
      ],
      "source": [
        "from sklearn.svm import SVC\n",
        "SVC_rbf = SVC(random_state = random_seed, kernel = 'rbf')\n",
        "\n",
        "SVC_rbf_scores = cross_val_score(SVC_rbf,\n",
        "                                    X_train,\n",
        "                                    Y_train,\n",
        "                                    scoring=scoring_parameter,\n",
        "                                    cv=k_fold)\n",
        "\n",
        "print(\"SVC kernel = rbf: \")\n",
        "print(\"- mean(): \" , SVC_rbf_scores.mean())\n",
        "print(\"- std(): \" , SVC_rbf_scores.std())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5M3wDyfXYdN2",
        "outputId": "03ad1ef4-afdc-48d0-9f42-1b9f0af07141"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "decision_tree: \n",
            "- mean():  0.5183615819209039\n",
            "- std():  0.05394992139310013\n"
          ]
        }
      ],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "decision_tree = DecisionTreeClassifier(random_state = random_seed)\n",
        "\n",
        "decision_tree_scores = cross_val_score(decision_tree,\n",
        "                                       X_train,\n",
        "                                       Y_train,\n",
        "                                       scoring=scoring_parameter,\n",
        "                                       cv=k_fold)\n",
        "\n",
        "print(\"decision_tree: \")\n",
        "print(\"- mean(): \" , decision_tree_scores.mean())\n",
        "print(\"- std(): \" , decision_tree_scores.std())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P6AWlOonYh0L",
        "outputId": "b9f4a00a-859f-4cb4-e688-5e6d43583fcb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "random_forest_classifier: \n",
            "- mean():  0.5924293785310735\n",
            "- std():  0.05399853287409892\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "random_forest_classifier = RandomForestClassifier(random_state = random_seed)\n",
        "\n",
        "random_forest_classifier_scores = cross_val_score(random_forest_classifier,\n",
        "                                       X_train,\n",
        "                                       Y_train,\n",
        "                                       scoring=scoring_parameter,\n",
        "                                       cv=k_fold)\n",
        "\n",
        "print(\"random_forest_classifier: \")\n",
        "print(\"- mean(): \" , random_forest_classifier_scores.mean())\n",
        "print(\"- std(): \" , random_forest_classifier_scores.std())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XlvnLPU1YlKx",
        "outputId": "c4b2d075-69f1-4f12-f1ec-a967a43aef2d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "extra_trees_classifier: \n",
            "- mean():  0.5655367231638417\n",
            "- std():  0.03734629944057303\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "extra_trees_classifier = ExtraTreesClassifier(random_state = random_seed)\n",
        "\n",
        "extra_trees_classifier_scores = cross_val_score(extra_trees_classifier,\n",
        "                                       X_train,\n",
        "                                       Y_train,\n",
        "                                       scoring=scoring_parameter,\n",
        "                                       cv=k_fold)\n",
        "\n",
        "print(\"extra_trees_classifier: \")\n",
        "print(\"- mean(): \" , extra_trees_classifier_scores.mean())\n",
        "print(\"- std(): \" , extra_trees_classifier_scores.std())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VUld2qs1Yo38",
        "outputId": "cce4da0c-e342-4691-8b77-7aa466a82658"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ada_boost_classifier: \n",
            "- mean():  0.5349717514124295\n",
            "- std():  0.07672066760789446\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "ada_boost_classifier = AdaBoostClassifier(random_state = random_seed)\n",
        "\n",
        "ada_boost_classifier_scores = cross_val_score(ada_boost_classifier,\n",
        "                                       X_train,\n",
        "                                       Y_train,\n",
        "                                       scoring=scoring_parameter,\n",
        "                                       cv=k_fold)\n",
        "\n",
        "print(\"ada_boost_classifier: \")\n",
        "print(\"- mean(): \" , ada_boost_classifier_scores.mean())\n",
        "print(\"- std(): \" , ada_boost_classifier_scores.std())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zh-9eXCCYtbb",
        "outputId": "c6a8a410-0db5-40bb-88d5-6d69f206ae50"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gradient_boosting_classifier: \n",
            "- mean():  0.5183050847457628\n",
            "- std():  0.04015093296211167\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "gradient_boosting_classifier = GradientBoostingClassifier(random_state = random_seed)\n",
        "\n",
        "gradient_boosting_classifier_scores = cross_val_score(gradient_boosting_classifier,\n",
        "                                       X_train,\n",
        "                                       Y_train,\n",
        "                                       scoring=scoring_parameter,\n",
        "                                       cv=k_fold)\n",
        "\n",
        "print(\"gradient_boosting_classifier: \")\n",
        "print(\"- mean(): \" , gradient_boosting_classifier_scores.mean())\n",
        "print(\"- std(): \" , gradient_boosting_classifier_scores.std())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xyeYVs-fY_tZ",
        "outputId": "9e957b49-0049-4b07-a675-9f7fe77b8efc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XGB_classifier: \n",
            "- mean():  0.5417514124293785\n",
            "- std():  0.0450103459863462\n"
          ]
        }
      ],
      "source": [
        "from xgboost import XGBClassifier\n",
        "\n",
        "XGB_classifier = XGBClassifier(random_state = random_seed)\n",
        "\n",
        "XGB_classifier_scores = cross_val_score(XGB_classifier,\n",
        "                                       X_train,\n",
        "                                       Y_train,\n",
        "                                       scoring=scoring_parameter,\n",
        "                                       cv=k_fold)\n",
        "\n",
        "print(\"XGB_classifier: \")\n",
        "print(\"- mean(): \" , XGB_classifier_scores.mean())\n",
        "print(\"- std(): \" , XGB_classifier_scores.std())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "98-GIi7vumU6"
      },
      "source": [
        "### Select the top best performing classifiers. List them along with their mean and std scores. For these top  performing classifiers, find the best hyperparameters using GridSearchCV.\n",
        "\n",
        "- Use estimator.get_params().keys() or the API description of the classifiers to find out which hyperparameters you can use.\n",
        "- Use the same scoring parameter ('accuracy') and use cv = 10 this time.\n",
        "- Print out the best_estimator_ and best_score_ results of your Grid Search\n",
        "- Use the following hyparparameter options if they fit for your top 3 classifiers:\n",
        "\n",
        "n_estimators = [100, 200, 300, 400, 500]\n",
        "\n",
        "learning_rate = [0.3, 0.1, 0.05]\n",
        "\n",
        "max_features =  [1, 0.7, 0.5, 0.4, 0.3]\n",
        "\n",
        "subsample = [1, 0.5, 0.3]\n",
        "\n",
        "max_samples = [1, 0.5, 0.3, 0.2]\n",
        "\n",
        "bootstrap = [True,False]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "f6q7KUMlZRc0"
      },
      "outputs": [],
      "source": [
        "n_estimators = [100, 200, 300, 400, 500]\n",
        "learning_rate = [0.3, 0.1, 0.05]\n",
        "max_features =  [1, 0.7, 0.5, 0.4, 0.3]\n",
        "subsample = [1, 0.5, 0.3]\n",
        "max_samples = [1, 0.5, 0.3, 0.2]\n",
        "bootstrap = [True,False]\n",
        "\n",
        "k_fold = 10\n",
        "\n",
        "from sklearn.model_selection import GridSearchCV"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ada_boost_classifier_scores:\n",
        "\n",
        "ada_grid_param = {'n_estimators' : n_estimators,\n",
        "              'learning_rate' : learning_rate\n",
        "              }\n",
        "\n",
        "ada_GridSearchCV = GridSearchCV(ada_boost_classifier,\n",
        "                    param_grid = ada_grid_param,\n",
        "                    cv = k_fold,\n",
        "                    scoring = scoring_parameter,\n",
        "                    verbose = 1)\n",
        "\n",
        "ada_GridSearchCV.fit(X_train, Y_train)\n",
        "\n",
        "ada_best_estimator = ada_GridSearchCV.best_estimator_\n",
        "ada_best_score = ada_GridSearchCV.best_score_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tbZzqi5JPIKP",
        "outputId": "afb2a72a-2aa5-4c8d-e494-6988116e4811"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 15 candidates, totalling 150 fits\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ada_best_score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f9dyJ-k7PXKf",
        "outputId": "6809dd39-071b-45b1-eedc-1738f2254819"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5826436781609197"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ada_best_estimator"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "ZzH5m93oPZrZ",
        "outputId": "2d06a955-20ca-4c3e-b8fd-73fe4f98f432"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AdaBoostClassifier(learning_rate=0.05, n_estimators=100, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier(learning_rate=0.05, n_estimators=100, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(learning_rate=0.05, n_estimators=100, random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7E306FlKx68C",
        "outputId": "eb72701c-553d-4254-9f19-7ac042fa1431"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 200 candidates, totalling 2000 fits\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:378: FitFailedWarning: \n",
            "1000 fits failed out of a total of 2000.\n",
            "The score on these train-test partitions for these parameters will be set to nan.\n",
            "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
            "\n",
            "Below are more details about the failures:\n",
            "--------------------------------------------------------------------------------\n",
            "1000 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py\", line 397, in fit\n",
            "    raise ValueError(\n",
            "ValueError: `max_sample` cannot be set if `bootstrap=False`. Either switch to `bootstrap=True` or set `max_sample=None`.\n",
            "\n",
            "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py:952: UserWarning: One or more of the test scores are non-finite: [0.53885057 0.53885057 0.53885057 0.53885057 0.53885057 0.58264368\n",
            " 0.59287356 0.58954023 0.59287356 0.58954023 0.59954023 0.5962069\n",
            " 0.58942529 0.58942529 0.58609195 0.5862069  0.58264368 0.59275862\n",
            " 0.60287356 0.5962069  0.53885057 0.53885057 0.53885057 0.53885057\n",
            " 0.53885057 0.58942529 0.57597701 0.57597701 0.58609195 0.58954023\n",
            " 0.59252874 0.60632184 0.59275862 0.5962069  0.59632184 0.60597701\n",
            " 0.61655172 0.61310345 0.59977011 0.59965517 0.53885057 0.53885057\n",
            " 0.53885057 0.53885057 0.53885057 0.57931034 0.58954023 0.59287356\n",
            " 0.59310345 0.59643678 0.58574713 0.59264368 0.60632184 0.59287356\n",
            " 0.59954023 0.59609195 0.60298851 0.59965517 0.59632184 0.59632184\n",
            " 0.53885057 0.53885057 0.53885057 0.53885057 0.53885057 0.57942529\n",
            " 0.58298851 0.58287356 0.58954023 0.58954023 0.59275862 0.6062069\n",
            " 0.59586207 0.60298851 0.59632184 0.59609195 0.59609195 0.58954023\n",
            " 0.59954023 0.5962069  0.53885057 0.53885057 0.53885057 0.53885057\n",
            " 0.53885057 0.58632184 0.57609195 0.57931034 0.58942529 0.58942529\n",
            " 0.59954023 0.59965517 0.59609195 0.58931034 0.58597701 0.59954023\n",
            " 0.59643678 0.58609195 0.58954023 0.5862069         nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan]\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "# random_forest_classifier_scores:\n",
        "\n",
        "rfc_grid_param = {'n_estimators' : n_estimators,\n",
        "              'bootstrap' : bootstrap,\n",
        "              'max_features': max_features,\n",
        "              'max_samples' : max_samples\n",
        "              }\n",
        "\n",
        "rfc_GridSearchCV = GridSearchCV(random_forest_classifier,\n",
        "                    param_grid = rfc_grid_param,\n",
        "                    cv = k_fold,\n",
        "                    scoring = scoring_parameter,\n",
        "                    verbose = 1)\n",
        "\n",
        "rfc_GridSearchCV.fit(X_train, Y_train)\n",
        "\n",
        "rfc_best_estimator = rfc_GridSearchCV.best_estimator_\n",
        "rfc_best_score = rfc_GridSearchCV.best_score_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rlNADegQ19VH",
        "outputId": "c31e4c78-d087-4de1-8b6c-e83e317b19ee"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.616551724137931"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "rfc_best_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "TwF7b4RE2BHj",
        "outputId": "654c1502-cde8-4c93-9ddc-2b5eac2d231c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_features=0.7, max_samples=0.2, n_estimators=200,\n",
              "                       random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_features=0.7, max_samples=0.2, n_estimators=200,\n",
              "                       random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_features=0.7, max_samples=0.2, n_estimators=200,\n",
              "                       random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "rfc_best_estimator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I49e8t9Iu_Ho",
        "outputId": "787c4ea1-42b3-4409-f102-d04ceece82ab"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fitting 10 folds for each of 225 candidates, totalling 2250 fits\n"
          ]
        }
      ],
      "source": [
        "# gradient_boosting_classifier:\n",
        "\n",
        "gbc_grid_param = {'n_estimators' : n_estimators,\n",
        "              'learning_rate' : learning_rate,\n",
        "              'max_features': max_features,\n",
        "              'subsample' : subsample\n",
        "              }\n",
        "\n",
        "gbc_GridSearchCV = GridSearchCV(gradient_boosting_classifier,\n",
        "                    param_grid = gbc_grid_param,\n",
        "                    cv = k_fold,\n",
        "                    scoring = scoring_parameter,\n",
        "                    verbose = 1)\n",
        "\n",
        "gbc_GridSearchCV.fit(X_train, Y_train)\n",
        "\n",
        "gbc_best_estimator = gbc_GridSearchCV.best_estimator_\n",
        "gbc_best_score = gbc_GridSearchCV.best_score_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "i5t89rRdZj7R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f9f4d620-e718-40da-9b00-b5b80f15d461"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5963218390804597"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "gbc_best_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "bh04ufFCZnp_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "outputId": "19f7e247-2855-4cfd-9689-985894900549"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingClassifier(learning_rate=0.05, max_features=1, random_state=42,\n",
              "                           subsample=0.3)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(learning_rate=0.05, max_features=1, random_state=42,\n",
              "                           subsample=0.3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(learning_rate=0.05, max_features=1, random_state=42,\n",
              "                           subsample=0.3)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "gbc_best_estimator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "oBmcHrTCZ6T6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "18200178-bf45-4ff9-cbd9-8754686c7caa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 5 candidates, totalling 50 fits\n"
          ]
        }
      ],
      "source": [
        "# XGB_classifier:\n",
        "xgb_grid_param = {'n_estimators' : n_estimators\n",
        "              }\n",
        "\n",
        "xgb_GridSearchCV = GridSearchCV(XGB_classifier,\n",
        "                    param_grid = xgb_grid_param,\n",
        "                    cv = k_fold,\n",
        "                    scoring = scoring_parameter,\n",
        "                    verbose = 1)\n",
        "\n",
        "xgb_GridSearchCV.fit(X_train, Y_train)\n",
        "\n",
        "xgb_best_estimator = xgb_GridSearchCV.best_estimator_\n",
        "xgb_best_score = xgb_GridSearchCV.best_score_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "sGx4C6b8Z_h_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "be9a36b1-f1f0-45e7-f203-c4eaab67caa6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5457471264367817"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "xgb_best_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "fu6f0fk8aCMz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "b0301cb6-94b2-4953-b243-2da984ed3c5a"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "              objective=&#x27;multi:softprob&#x27;, predictor=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "              objective=&#x27;multi:softprob&#x27;, predictor=None, ...)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "              objective='multi:softprob', predictor=None, ...)"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "xgb_best_estimator"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "esoSP9yQaiuQ"
      },
      "source": [
        "### Plot Learning Curve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "snY12Dhva-9l"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.model_selection import learning_curve\n",
        "\n",
        "# Plot learning curve\n",
        "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None, n_jobs=-1, train_sizes=np.linspace(.1, 1.0, 5)):\n",
        "\n",
        "# Generate a simple plot of the test and traning learning curve.\n",
        "# Parameters\n",
        "#----------\n",
        "#estimator : object type that implements the \"fit\" and \"predict\" methods An object of that type which is cloned for each validation.\n",
        "#title : string\n",
        "#Title for the chart.\n",
        "#X : array-like, shape (n_samples, n_features)\n",
        "#Training vector, where n_samples is the number of samples and n_features is the number of features.\n",
        "#y : array-like, shape (n_samples) or (n_samples, n_features), optional Target relative to X for classification or regression;\n",
        "#None for unsupervised learning.\n",
        "#ylim : tuple, shape (ymin, ymax), optional Defines minimum and maximum yvalues plotted.\n",
        "#cv : integer, cross-validation generator, optional\n",
        "#If an integer is passed, it is the number of folds (defaults to 3). Specific cross-validation objects can be passed, see sklearn.cross_validation module for the list of possible objects\n",
        "#n_jobs : integer, optional\n",
        "#Number of jobs to run in parallel (default 1).\n",
        "#x1 = np.linspace(0, 10, 8, endpoint=True) produces 8 evenly spaced points in the range 0 to 10\n",
        "\n",
        "  plt.figure()\n",
        "  plt.title(title)\n",
        "  if ylim is not None: plt.ylim(*ylim)\n",
        "\n",
        "  plt.xlabel(\"Training examples\")\n",
        "  plt.ylabel(\"Score\")\n",
        "  train_sizes, train_scores, test_scores = learning_curve(  estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
        "  train_scores_mean = np.mean(train_scores, axis=1)\n",
        "  train_scores_std = np.std(train_scores, axis=1)\n",
        "  test_scores_mean = np.mean(test_scores, axis=1)\n",
        "  test_scores_std = np.std(test_scores, axis=1)\n",
        "  plt.grid()\n",
        "\n",
        "  plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color=\"r\")\n",
        "  plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
        "  plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\", label=\"Training score\")\n",
        "  plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
        "\n",
        "  label=\"Cross-validation score\")\n",
        "\n",
        "  plt.legend(loc=\"best\")\n",
        "  return plt\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "gc1sKzsjbKZp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "a049bc38-b1fc-4b69-a5d6-17e7d23af676"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Gradient boosting - Learning Curve\n",
        "plot_learning_curve(estimator = gbc_best_estimator,title = \"GBC learning curve\", X = X_train, y = Y_train, cv = k_fold);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "1ZkRTxdfbNlM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "b76f3536-b67f-4471-eff0-aa0f6cd4dd7f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Random Forest - Learning Curve\n",
        "plot_learning_curve(estimator = rfc_best_estimator,title = \"Random Forest learning curve\", X = X_train, y = Y_train, cv = k_fold);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "1WGPUfK6bRDY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "84c37bad-3506-4b6b-cc74-fbc0a3e2ab5c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# XG boosting - Learning Curve\n",
        "plot_learning_curve(estimator = xgb_best_estimator,title = \"XGBoosting learning curve\", X = X_train, y = Y_train, cv = k_fold);"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Adaboost - Learning Curve\n",
        "plot_learning_curve(estimator = ada_best_estimator,title = \"AdaBoost learning curve\", X = X_train, y = Y_train, cv = k_fold);"
      ],
      "metadata": {
        "id": "CqR54SZsQWg0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "ba5eaf01-6eeb-40ba-efdb-b4a66ed50de8"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q1rf2O7pu_q8"
      },
      "source": [
        "### Use VotingClassifier of sklearn to create an ensemble learner that utilizes the top estimators you found above. Set the voting parameter to 'soft'. Use cross_val_score to calculate its score and print out its mean and std values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "0WukqonzvLfN"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import VotingClassifier\n",
        "\n",
        "VotingPredictor = VotingClassifier(estimators =\n",
        "                           [('gbc', gbc_best_estimator),\n",
        "                            ('rfc', rfc_best_estimator),\n",
        "                            ('xgb', xgb_best_estimator),\n",
        "                            ('ada',ada_best_estimator),\n",
        "                            ('sgd',SGD_classifier),\n",
        "                            ('svc',SVC_linear),\n",
        "                            ('log',logistic_regression)\n",
        "                            ],\n",
        "                           voting='hard') #'soft' parameter cannot be used since probability estimates are not available\n",
        "\n",
        "VotingPredictor = VotingPredictor.fit(X_train, Y_train)\n",
        "\n",
        "emsemble_scores = cross_val_score(VotingPredictor, X_train, Y_train, cv = k_fold, scoring = scoring_parameter)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"emsemble: \")\n",
        "print(\"- mean(): \" , emsemble_scores.mean())\n",
        "print(\"- std(): \" , emsemble_scores.std())"
      ],
      "metadata": {
        "id": "-kyHB5FwQ8Lz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "29ae96b5-9e98-42fc-958f-b326bc5ed1c4"
      },
      "execution_count": 55,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "emsemble: \n",
            "- mean():  0.6026436781609196\n",
            "- std():  0.0582205182804479\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "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": "3.8.3"
    },
    "nav_menu": {},
    "toc": {
      "navigate_menu": true,
      "number_sections": true,
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      "threshold": 6,
      "toc_cell": false,
      "toc_section_display": "block",
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