ENSEMBLE_MODEL_Stacking.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#ENSEMBLE MODEL: **Stacking**"
      ],
      "metadata": {
        "id": "pqpdJrxMX6PZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Bagging & Boosting**\n",
        "\n",
        "\n",
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "MnGcnxNzbNr6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In *Bagging*, we build models in *parallel* and combine the output using voting mechanism to get the final prediction.\n",
        "\n",
        "In *Boosting*, we build models in *sequennce* and then keep on increasing the weight of wrongly classified items to increase their chance of being predicted right."
      ],
      "metadata": {
        "id": "Itti2R7Bbj17"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "\n",
        "### **Stacking**"
      ],
      "metadata": {
        "id": "pYZyoxK6bZ87"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "oZaFqRgiajec"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In *Stacking*, we stack several different models and use their predictions as target in our final model (also known as meta model)."
      ],
      "metadata": {
        "id": "fdiTImkucKZt"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bgcseXc0-ZMa"
      },
      "source": [
        "from sklearn import datasets\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import StackingClassifier\n",
        "import xgboost\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.model_selection import cross_val_score"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q0Fg2iqK-yxU"
      },
      "source": [
        "df = datasets.load_breast_cancer()"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kL-UMSpTYdkL",
        "outputId": "a67842d3-2a39-4e64-f703-654279876be1"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'data': array([[1.799e+01, 1.038e+01, 1.228e+02, ..., 2.654e-01, 4.601e-01,\n",
              "         1.189e-01],\n",
              "        [2.057e+01, 1.777e+01, 1.329e+02, ..., 1.860e-01, 2.750e-01,\n",
              "         8.902e-02],\n",
              "        [1.969e+01, 2.125e+01, 1.300e+02, ..., 2.430e-01, 3.613e-01,\n",
              "         8.758e-02],\n",
              "        ...,\n",
              "        [1.660e+01, 2.808e+01, 1.083e+02, ..., 1.418e-01, 2.218e-01,\n",
              "         7.820e-02],\n",
              "        [2.060e+01, 2.933e+01, 1.401e+02, ..., 2.650e-01, 4.087e-01,\n",
              "         1.240e-01],\n",
              "        [7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,\n",
              "         7.039e-02]]),\n",
              " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
              "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,\n",
              "        1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,\n",
              "        1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,\n",
              "        0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n",
              "        1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,\n",
              "        0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n",
              "        1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1,\n",
              "        1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0,\n",
              "        0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
              "        0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
              "        1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,\n",
              "        1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,\n",
              "        1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
              "        1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
              "        1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n",
              "        1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1]),\n",
              " 'frame': None,\n",
              " 'target_names': array(['malignant', 'benign'], dtype='<U9'),\n",
              " 'DESCR': '.. _breast_cancer_dataset:\\n\\nBreast cancer wisconsin (diagnostic) dataset\\n--------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 569\\n\\n    :Number of Attributes: 30 numeric, predictive attributes and the class\\n\\n    :Attribute Information:\\n        - radius (mean of distances from center to points on the perimeter)\\n        - texture (standard deviation of gray-scale values)\\n        - perimeter\\n        - area\\n        - smoothness (local variation in radius lengths)\\n        - compactness (perimeter^2 / area - 1.0)\\n        - concavity (severity of concave portions of the contour)\\n        - concave points (number of concave portions of the contour)\\n        - symmetry\\n        - fractal dimension (\"coastline approximation\" - 1)\\n\\n        The mean, standard error, and \"worst\" or largest (mean of the three\\n        worst/largest values) of these features were computed for each image,\\n        resulting in 30 features.  For instance, field 0 is Mean Radius, field\\n        10 is Radius SE, field 20 is Worst Radius.\\n\\n        - class:\\n                - WDBC-Malignant\\n                - WDBC-Benign\\n\\n    :Summary Statistics:\\n\\n    ===================================== ====== ======\\n                                           Min    Max\\n    ===================================== ====== ======\\n    radius (mean):                        6.981  28.11\\n    texture (mean):                       9.71   39.28\\n    perimeter (mean):                     43.79  188.5\\n    area (mean):                          143.5  2501.0\\n    smoothness (mean):                    0.053  0.163\\n    compactness (mean):                   0.019  0.345\\n    concavity (mean):                     0.0    0.427\\n    concave points (mean):                0.0    0.201\\n    symmetry (mean):                      0.106  0.304\\n    fractal dimension (mean):             0.05   0.097\\n    radius (standard error):              0.112  2.873\\n    texture (standard error):             0.36   4.885\\n    perimeter (standard error):           0.757  21.98\\n    area (standard error):                6.802  542.2\\n    smoothness (standard error):          0.002  0.031\\n    compactness (standard error):         0.002  0.135\\n    concavity (standard error):           0.0    0.396\\n    concave points (standard error):      0.0    0.053\\n    symmetry (standard error):            0.008  0.079\\n    fractal dimension (standard error):   0.001  0.03\\n    radius (worst):                       7.93   36.04\\n    texture (worst):                      12.02  49.54\\n    perimeter (worst):                    50.41  251.2\\n    area (worst):                         185.2  4254.0\\n    smoothness (worst):                   0.071  0.223\\n    compactness (worst):                  0.027  1.058\\n    concavity (worst):                    0.0    1.252\\n    concave points (worst):               0.0    0.291\\n    symmetry (worst):                     0.156  0.664\\n    fractal dimension (worst):            0.055  0.208\\n    ===================================== ====== ======\\n\\n    :Missing Attribute Values: None\\n\\n    :Class Distribution: 212 - Malignant, 357 - Benign\\n\\n    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\\n\\n    :Donor: Nick Street\\n\\n    :Date: November, 1995\\n\\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\\nhttps://goo.gl/U2Uwz2\\n\\nFeatures are computed from a digitized image of a fine needle\\naspirate (FNA) of a breast mass.  They describe\\ncharacteristics of the cell nuclei present in the image.\\n\\nSeparating plane described above was obtained using\\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\\nConstruction Via Linear Programming.\" Proceedings of the 4th\\nMidwest Artificial Intelligence and Cognitive Science Society,\\npp. 97-101, 1992], a classification method which uses linear\\nprogramming to construct a decision tree.  Relevant features\\nwere selected using an exhaustive search in the space of 1-4\\nfeatures and 1-3 separating planes.\\n\\nThe actual linear program used to obtain the separating plane\\nin the 3-dimensional space is that described in:\\n[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\\nProgramming Discrimination of Two Linearly Inseparable Sets\",\\nOptimization Methods and Software 1, 1992, 23-34].\\n\\nThis database is also available through the UW CS ftp server:\\n\\nftp ftp.cs.wisc.edu\\ncd math-prog/cpo-dataset/machine-learn/WDBC/\\n\\n.. topic:: References\\n\\n   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \\n     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \\n     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\\n     San Jose, CA, 1993.\\n   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \\n     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \\n     July-August 1995.\\n   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\\n     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \\n     163-171.',\n",
              " 'feature_names': array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
              "        'mean smoothness', 'mean compactness', 'mean concavity',\n",
              "        'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
              "        'radius error', 'texture error', 'perimeter error', 'area error',\n",
              "        'smoothness error', 'compactness error', 'concavity error',\n",
              "        'concave points error', 'symmetry error',\n",
              "        'fractal dimension error', 'worst radius', 'worst texture',\n",
              "        'worst perimeter', 'worst area', 'worst smoothness',\n",
              "        'worst compactness', 'worst concavity', 'worst concave points',\n",
              "        'worst symmetry', 'worst fractal dimension'], dtype='<U23'),\n",
              " 'filename': 'breast_cancer.csv',\n",
              " 'data_module': 'sklearn.datasets.data'}"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.feature_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nn6ySq1kYu_a",
        "outputId": "eeaff0d3-f586-43ab-a137-b8c445980737"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
              "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
              "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
              "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
              "       'smoothness error', 'compactness error', 'concavity error',\n",
              "       'concave points error', 'symmetry error',\n",
              "       'fractal dimension error', 'worst radius', 'worst texture',\n",
              "       'worst perimeter', 'worst area', 'worst smoothness',\n",
              "       'worst compactness', 'worst concavity', 'worst concave points',\n",
              "       'worst symmetry', 'worst fractal dimension'], dtype='<U23')"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "weCDr33A_lW3",
        "outputId": "d91e861c-cbbf-40a2-d4bf-7982bd5de228"
      },
      "source": [
        "df.target_names"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['malignant', 'benign'], dtype='<U9')"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tHwOippK_Lfg"
      },
      "source": [
        "X = pd.DataFrame(columns = df.feature_names, data = df.data)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "ict9RBWVev2O",
        "outputId": "666f8acb-9684-45fe-f3cb-6c40e3eff379"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
              "0          17.99         10.38          122.80     1001.0          0.11840   \n",
              "1          20.57         17.77          132.90     1326.0          0.08474   \n",
              "2          19.69         21.25          130.00     1203.0          0.10960   \n",
              "3          11.42         20.38           77.58      386.1          0.14250   \n",
              "4          20.29         14.34          135.10     1297.0          0.10030   \n",
              "..           ...           ...             ...        ...              ...   \n",
              "564        21.56         22.39          142.00     1479.0          0.11100   \n",
              "565        20.13         28.25          131.20     1261.0          0.09780   \n",
              "566        16.60         28.08          108.30      858.1          0.08455   \n",
              "567        20.60         29.33          140.10     1265.0          0.11780   \n",
              "568         7.76         24.54           47.92      181.0          0.05263   \n",
              "\n",
              "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "0             0.27760         0.30010              0.14710         0.2419   \n",
              "1             0.07864         0.08690              0.07017         0.1812   \n",
              "2             0.15990         0.19740              0.12790         0.2069   \n",
              "3             0.28390         0.24140              0.10520         0.2597   \n",
              "4             0.13280         0.19800              0.10430         0.1809   \n",
              "..                ...             ...                  ...            ...   \n",
              "564           0.11590         0.24390              0.13890         0.1726   \n",
              "565           0.10340         0.14400              0.09791         0.1752   \n",
              "566           0.10230         0.09251              0.05302         0.1590   \n",
              "567           0.27700         0.35140              0.15200         0.2397   \n",
              "568           0.04362         0.00000              0.00000         0.1587   \n",
              "\n",
              "     mean fractal dimension  ...  worst radius  worst texture  \\\n",
              "0                   0.07871  ...        25.380          17.33   \n",
              "1                   0.05667  ...        24.990          23.41   \n",
              "2                   0.05999  ...        23.570          25.53   \n",
              "3                   0.09744  ...        14.910          26.50   \n",
              "4                   0.05883  ...        22.540          16.67   \n",
              "..                      ...  ...           ...            ...   \n",
              "564                 0.05623  ...        25.450          26.40   \n",
              "565                 0.05533  ...        23.690          38.25   \n",
              "566                 0.05648  ...        18.980          34.12   \n",
              "567                 0.07016  ...        25.740          39.42   \n",
              "568                 0.05884  ...         9.456          30.37   \n",
              "\n",
              "     worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
              "0             184.60      2019.0           0.16220            0.66560   \n",
              "1             158.80      1956.0           0.12380            0.18660   \n",
              "2             152.50      1709.0           0.14440            0.42450   \n",
              "3              98.87       567.7           0.20980            0.86630   \n",
              "4             152.20      1575.0           0.13740            0.20500   \n",
              "..               ...         ...               ...                ...   \n",
              "564           166.10      2027.0           0.14100            0.21130   \n",
              "565           155.00      1731.0           0.11660            0.19220   \n",
              "566           126.70      1124.0           0.11390            0.30940   \n",
              "567           184.60      1821.0           0.16500            0.86810   \n",
              "568            59.16       268.6           0.08996            0.06444   \n",
              "\n",
              "     worst concavity  worst concave points  worst symmetry  \\\n",
              "0             0.7119                0.2654          0.4601   \n",
              "1             0.2416                0.1860          0.2750   \n",
              "2             0.4504                0.2430          0.3613   \n",
              "3             0.6869                0.2575          0.6638   \n",
              "4             0.4000                0.1625          0.2364   \n",
              "..               ...                   ...             ...   \n",
              "564           0.4107                0.2216          0.2060   \n",
              "565           0.3215                0.1628          0.2572   \n",
              "566           0.3403                0.1418          0.2218   \n",
              "567           0.9387                0.2650          0.4087   \n",
              "568           0.0000                0.0000          0.2871   \n",
              "\n",
              "     worst fractal dimension  \n",
              "0                    0.11890  \n",
              "1                    0.08902  \n",
              "2                    0.08758  \n",
              "3                    0.17300  \n",
              "4                    0.07678  \n",
              "..                       ...  \n",
              "564                  0.07115  \n",
              "565                  0.06637  \n",
              "566                  0.07820  \n",
              "567                  0.12400  \n",
              "568                  0.07039  \n",
              "\n",
              "[569 rows x 30 columns]"
            ],
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              "  <div id=\"df-6099ad3b-c12c-4116-8ce0-737abcfd400b\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</th>\n",
              "      <th>worst radius</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.30010</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>...</td>\n",
              "      <td>25.380</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.16220</td>\n",
              "      <td>0.66560</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.08690</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>...</td>\n",
              "      <td>24.990</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.12380</td>\n",
              "      <td>0.18660</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.19740</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>...</td>\n",
              "      <td>23.570</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.14440</td>\n",
              "      <td>0.42450</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.24140</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>...</td>\n",
              "      <td>14.910</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.20980</td>\n",
              "      <td>0.86630</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.19800</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>...</td>\n",
              "      <td>22.540</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.13740</td>\n",
              "      <td>0.20500</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>564</th>\n",
              "      <td>21.56</td>\n",
              "      <td>22.39</td>\n",
              "      <td>142.00</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>0.11100</td>\n",
              "      <td>0.11590</td>\n",
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              "      <td>0.1726</td>\n",
              "      <td>0.05623</td>\n",
              "      <td>...</td>\n",
              "      <td>25.450</td>\n",
              "      <td>26.40</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.14100</td>\n",
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              "      <td>0.4107</td>\n",
              "      <td>0.2216</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.07115</td>\n",
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              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>20.13</td>\n",
              "      <td>28.25</td>\n",
              "      <td>131.20</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.09780</td>\n",
              "      <td>0.10340</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.09791</td>\n",
              "      <td>0.1752</td>\n",
              "      <td>0.05533</td>\n",
              "      <td>...</td>\n",
              "      <td>23.690</td>\n",
              "      <td>38.25</td>\n",
              "      <td>155.00</td>\n",
              "      <td>1731.0</td>\n",
              "      <td>0.11660</td>\n",
              "      <td>0.19220</td>\n",
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              "    <tr>\n",
              "      <th>566</th>\n",
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              "      <td>28.08</td>\n",
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              "      <td>858.1</td>\n",
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              "      <td>0.10230</td>\n",
              "      <td>0.09251</td>\n",
              "      <td>0.05302</td>\n",
              "      <td>0.1590</td>\n",
              "      <td>0.05648</td>\n",
              "      <td>...</td>\n",
              "      <td>18.980</td>\n",
              "      <td>34.12</td>\n",
              "      <td>126.70</td>\n",
              "      <td>1124.0</td>\n",
              "      <td>0.11390</td>\n",
              "      <td>0.30940</td>\n",
              "      <td>0.3403</td>\n",
              "      <td>0.1418</td>\n",
              "      <td>0.2218</td>\n",
              "      <td>0.07820</td>\n",
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              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>20.60</td>\n",
              "      <td>29.33</td>\n",
              "      <td>140.10</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>0.11780</td>\n",
              "      <td>0.27700</td>\n",
              "      <td>0.35140</td>\n",
              "      <td>0.15200</td>\n",
              "      <td>0.2397</td>\n",
              "      <td>0.07016</td>\n",
              "      <td>...</td>\n",
              "      <td>25.740</td>\n",
              "      <td>39.42</td>\n",
              "      <td>184.60</td>\n",
              "      <td>1821.0</td>\n",
              "      <td>0.16500</td>\n",
              "      <td>0.86810</td>\n",
              "      <td>0.9387</td>\n",
              "      <td>0.2650</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.12400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>7.76</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>0.05884</td>\n",
              "      <td>...</td>\n",
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              "      <td>0.0000</td>\n",
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              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
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          },
          "metadata": {},
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      ]
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    {
      "cell_type": "code",
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        "y = df.target"
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    {
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              "mean radius                0\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      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-573d2667-a212-4b4e-8371-b08e1bce2cbb 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-573d2667-a212-4b4e-8371-b08e1bce2cbb');\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": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oRXI50rnCkzh",
        "outputId": "93058342-3005-4c02-a67e-9971cb3a3349"
      },
      "source": [
        "y.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "target\n",
              "1         357\n",
              "0         212\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZSPZhCAlIulM"
      },
      "source": [
        "The data looks balanced, so we will choose accuracy as our metric."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0CFZr99nEaRd"
      },
      "source": [
        "Here,\n",
        "*   1 - Benign\n",
        "*   0 - Malignant\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QN54fbppIhIM"
      },
      "source": [
        "y = y['target']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WobhAN5EPtx9"
      },
      "source": [
        "# Stacking"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "azi4LZ51QapT"
      },
      "source": [
        "dtc =  DecisionTreeClassifier()\n",
        "rfc = RandomForestClassifier()\n",
        "knn =  KNeighborsClassifier()\n",
        "xgb = xgboost.XGBClassifier()\n",
        "clf = [('dtc',dtc),('rfc',rfc),('knn',knn),('xgb',xgb)] #list of (str, estimator)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qgtu9owmJRfo",
        "outputId": "3124bbb9-c48f-4e69-d0b6-7a18ad848247"
      },
      "source": [
        "lr = LogisticRegression()\n",
        "stack_model = StackingClassifier( estimators = clf,final_estimator = lr)\n",
        "score = cross_val_score(stack_model,X,y,cv = 5,scoring = 'accuracy')\n",
        "print(\"The final accuracy score is: \",score.mean())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The final accuracy score is:  0.9683744760130415\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mdyx3GrBRkMM"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}