Xgboost_pyds.ipynb

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  "metadata": {
    "colab": {
      "name": "Xgboost_pyds.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOOebu66vhkSKOna9h6HbI1",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/FacundoRo/XGBoostPython/blob/master/Xgboost_pyds.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AxrwjH9CplMM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "ed920208-ca97-4722-baf1-d297e78a8244"
      },
      "source": [
        "!pip install xgboost"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: xgboost in /usr/local/lib/python3.6/dist-packages (0.90)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from xgboost) (1.18.4)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from xgboost) (1.4.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jSC-l12Lpzwr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import xgboost as xgb"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zErUHMkXqqbH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.datasets import load_boston\n",
        "boston = load_boston()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_InRFksktfTG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5cde06cb-e0d8-4819-c9e8-6363916708c9"
      },
      "source": [
        "boston"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'DESCR': \".. _boston_dataset:\\n\\nBoston house prices dataset\\n---------------------------\\n\\n**Data Set Characteristics:**  \\n\\n    :Number of Instances: 506 \\n\\n    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\\n\\n    :Attribute Information (in order):\\n        - CRIM     per capita crime rate by town\\n        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\\n        - INDUS    proportion of non-retail business acres per town\\n        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\\n        - NOX      nitric oxides concentration (parts per 10 million)\\n        - RM       average number of rooms per dwelling\\n        - AGE      proportion of owner-occupied units built prior to 1940\\n        - DIS      weighted distances to five Boston employment centres\\n        - RAD      index of accessibility to radial highways\\n        - TAX      full-value property-tax rate per $10,000\\n        - PTRATIO  pupil-teacher ratio by town\\n        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\\n        - LSTAT    % lower status of the population\\n        - MEDV     Median value of owner-occupied homes in $1000's\\n\\n    :Missing Attribute Values: None\\n\\n    :Creator: Harrison, D. and Rubinfeld, D.L.\\n\\nThis is a copy of UCI ML housing dataset.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/housing/\\n\\n\\nThis dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\\n\\nThe Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\\nprices and the demand for clean air', J. Environ. Economics & Management,\\nvol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\\n...', Wiley, 1980.   N.B. Various transformations are used in the table on\\npages 244-261 of the latter.\\n\\nThe Boston house-price data has been used in many machine learning papers that address regression\\nproblems.   \\n     \\n.. topic:: References\\n\\n   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\\n   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\\n\",\n",
              " 'data': array([[6.3200e-03, 1.8000e+01, 2.3100e+00, ..., 1.5300e+01, 3.9690e+02,\n",
              "         4.9800e+00],\n",
              "        [2.7310e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9690e+02,\n",
              "         9.1400e+00],\n",
              "        [2.7290e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9283e+02,\n",
              "         4.0300e+00],\n",
              "        ...,\n",
              "        [6.0760e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
              "         5.6400e+00],\n",
              "        [1.0959e-01, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9345e+02,\n",
              "         6.4800e+00],\n",
              "        [4.7410e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
              "         7.8800e+00]]),\n",
              " 'feature_names': array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
              "        'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7'),\n",
              " 'filename': '/usr/local/lib/python3.6/dist-packages/sklearn/datasets/data/boston_house_prices.csv',\n",
              " 'target': array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\n",
              "        18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\n",
              "        15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\n",
              "        13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\n",
              "        21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\n",
              "        35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,\n",
              "        19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,\n",
              "        20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,\n",
              "        23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,\n",
              "        33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,\n",
              "        21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,\n",
              "        20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,\n",
              "        23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,\n",
              "        15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,\n",
              "        17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\n",
              "        25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\n",
              "        23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\n",
              "        32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\n",
              "        34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\n",
              "        20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,\n",
              "        26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,\n",
              "        31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,\n",
              "        22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\n",
              "        42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\n",
              "        36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\n",
              "        32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\n",
              "        20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\n",
              "        20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\n",
              "        22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\n",
              "        21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\n",
              "        19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\n",
              "        32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\n",
              "        18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\n",
              "        16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\n",
              "        13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3,  8.8,\n",
              "         7.2, 10.5,  7.4, 10.2, 11.5, 15.1, 23.2,  9.7, 13.8, 12.7, 13.1,\n",
              "        12.5,  8.5,  5. ,  6.3,  5.6,  7.2, 12.1,  8.3,  8.5,  5. , 11.9,\n",
              "        27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3,  7. ,  7.2,  7.5, 10.4,\n",
              "         8.8,  8.4, 16.7, 14.2, 20.8, 13.4, 11.7,  8.3, 10.2, 10.9, 11. ,\n",
              "         9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4,  9.6,  8.7,  8.4, 12.8,\n",
              "        10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\n",
              "        15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\n",
              "        19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\n",
              "        29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\n",
              "        20.6, 21.2, 19.1, 20.6, 15.2,  7. ,  8.1, 13.6, 20.1, 21.8, 24.5,\n",
              "        23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9])}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BWgfnt5stgQT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f7c4524b-e818-4a14-980f-438f95ff7873"
      },
      "source": [
        "boston.keys()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AcmK5b5Vtq7v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "926bef32-f607-40d8-e8c9-3a2114329f06"
      },
      "source": [
        "boston.DESCR"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\".. _boston_dataset:\\n\\nBoston house prices dataset\\n---------------------------\\n\\n**Data Set Characteristics:**  \\n\\n    :Number of Instances: 506 \\n\\n    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\\n\\n    :Attribute Information (in order):\\n        - CRIM     per capita crime rate by town\\n        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\\n        - INDUS    proportion of non-retail business acres per town\\n        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\\n        - NOX      nitric oxides concentration (parts per 10 million)\\n        - RM       average number of rooms per dwelling\\n        - AGE      proportion of owner-occupied units built prior to 1940\\n        - DIS      weighted distances to five Boston employment centres\\n        - RAD      index of accessibility to radial highways\\n        - TAX      full-value property-tax rate per $10,000\\n        - PTRATIO  pupil-teacher ratio by town\\n        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\\n        - LSTAT    % lower status of the population\\n        - MEDV     Median value of owner-occupied homes in $1000's\\n\\n    :Missing Attribute Values: None\\n\\n    :Creator: Harrison, D. and Rubinfeld, D.L.\\n\\nThis is a copy of UCI ML housing dataset.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/housing/\\n\\n\\nThis dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\\n\\nThe Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\\nprices and the demand for clean air', J. Environ. Economics & Management,\\nvol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\\n...', Wiley, 1980.   N.B. Various transformations are used in the table on\\npages 244-261 of the latter.\\n\\nThe Boston house-price data has been used in many machine learning papers that address regression\\nproblems.   \\n     \\n.. topic:: References\\n\\n   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\\n   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\\n\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j4RTGGoFuBke",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d0ea19ff-b285-4724-f31a-c69add439eee"
      },
      "source": [
        "boston.keys"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function Bunch.keys>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U_lYyM7fuDyI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "bbb3dd2d-048c-425f-904c-4a09602be396"
      },
      "source": [
        "boston.data"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6.3200e-03, 1.8000e+01, 2.3100e+00, ..., 1.5300e+01, 3.9690e+02,\n",
              "        4.9800e+00],\n",
              "       [2.7310e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9690e+02,\n",
              "        9.1400e+00],\n",
              "       [2.7290e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9283e+02,\n",
              "        4.0300e+00],\n",
              "       ...,\n",
              "       [6.0760e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
              "        5.6400e+00],\n",
              "       [1.0959e-01, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9345e+02,\n",
              "        6.4800e+00],\n",
              "       [4.7410e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
              "        7.8800e+00]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ykZs1PCCuLNM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "27c534ef-8e0c-41e5-fa5b-486b0734bc4b"
      },
      "source": [
        "boston.data.shape"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(506, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "doYnBphuuNMB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xJE2RoaWuk_5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = pd.DataFrame(boston.data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DErQrFD6urOZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "outputId": "331e8d72-7038-4958-c482-f5dab08eca89"
      },
      "source": [
        "data"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0.00632</td>\n",
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              "      <td>2.31</td>\n",
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              "      <td>2.0</td>\n",
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              "          0     1      2    3      4   ...   8      9     10      11    12\n",
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              "\n",
              "[506 rows x 13 columns]"
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    {
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      "metadata": {
        "id": "-CxXejsHusKt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data.columns = boston.feature_names"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
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        "id": "zX2cVz8Ou36s",
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        "data"
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      "execution_count": 14,
      "outputs": [
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              "        CRIM    ZN  INDUS  CHAS    NOX  ...  RAD    TAX  PTRATIO       B  LSTAT\n",
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              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4690</td>\n",
              "      <td>6.421</td>\n",
              "      <td>78.9</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>346</th>\n",
              "      <td>0.06162</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.39</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>5.898</td>\n",
              "      <td>52.3</td>\n",
              "      <td>8.0136</td>\n",
              "      <td>3.0</td>\n",
              "      <td>352.0</td>\n",
              "      <td>18.8</td>\n",
              "      <td>364.61</td>\n",
              "      <td>12.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>259</th>\n",
              "      <td>0.65665</td>\n",
              "      <td>20.0</td>\n",
              "      <td>3.97</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.6470</td>\n",
              "      <td>6.842</td>\n",
              "      <td>100.0</td>\n",
              "      <td>2.0107</td>\n",
              "      <td>5.0</td>\n",
              "      <td>264.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>391.93</td>\n",
              "      <td>6.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>0.12204</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.89</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4450</td>\n",
              "      <td>6.625</td>\n",
              "      <td>57.8</td>\n",
              "      <td>3.4952</td>\n",
              "      <td>2.0</td>\n",
              "      <td>276.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>357.98</td>\n",
              "      <td>6.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>331</th>\n",
              "      <td>0.05023</td>\n",
              "      <td>35.0</td>\n",
              "      <td>6.06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4379</td>\n",
              "      <td>5.706</td>\n",
              "      <td>28.4</td>\n",
              "      <td>6.6407</td>\n",
              "      <td>1.0</td>\n",
              "      <td>304.0</td>\n",
              "      <td>16.9</td>\n",
              "      <td>394.02</td>\n",
              "      <td>12.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>1.22358</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19.58</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.6050</td>\n",
              "      <td>6.943</td>\n",
              "      <td>97.4</td>\n",
              "      <td>1.8773</td>\n",
              "      <td>5.0</td>\n",
              "      <td>403.0</td>\n",
              "      <td>14.7</td>\n",
              "      <td>363.43</td>\n",
              "      <td>4.59</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        CRIM    ZN  INDUS  CHAS     NOX  ...  RAD    TAX  PTRATIO       B  LSTAT\n",
              "191  0.06911  45.0   3.44   0.0  0.4370  ...  5.0  398.0     15.2  389.71   4.69\n",
              "198  0.03768  80.0   1.52   0.0  0.4040  ...  2.0  329.0     12.6  392.20   6.62\n",
              "1    0.02731   0.0   7.07   0.0  0.4690  ...  2.0  242.0     17.8  396.90   9.14\n",
              "346  0.06162   0.0   4.39   0.0  0.4420  ...  3.0  352.0     18.8  364.61  12.67\n",
              "259  0.65665  20.0   3.97   0.0  0.6470  ...  5.0  264.0     13.0  391.93   6.90\n",
              "95   0.12204   0.0   2.89   0.0  0.4450  ...  2.0  276.0     18.0  357.98   6.65\n",
              "331  0.05023  35.0   6.06   0.0  0.4379  ...  1.0  304.0     16.9  394.02  12.43\n",
              "157  1.22358   0.0  19.58   0.0  0.6050  ...  5.0  403.0     14.7  363.43   4.59\n",
              "\n",
              "[8 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EexoGjE7u_9P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data['PRICE'] = boston.target"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eBxE0pRlvJU3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "outputId": "2ef6f32d-14e3-49f4-879a-99d5dc1c1e00"
      },
      "source": [
        "data.info()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 506 entries, 0 to 505\n",
            "Data columns (total 14 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   CRIM     506 non-null    float64\n",
            " 1   ZN       506 non-null    float64\n",
            " 2   INDUS    506 non-null    float64\n",
            " 3   CHAS     506 non-null    float64\n",
            " 4   NOX      506 non-null    float64\n",
            " 5   RM       506 non-null    float64\n",
            " 6   AGE      506 non-null    float64\n",
            " 7   DIS      506 non-null    float64\n",
            " 8   RAD      506 non-null    float64\n",
            " 9   TAX      506 non-null    float64\n",
            " 10  PTRATIO  506 non-null    float64\n",
            " 11  B        506 non-null    float64\n",
            " 12  LSTAT    506 non-null    float64\n",
            " 13  PRICE    506 non-null    float64\n",
            "dtypes: float64(14)\n",
            "memory usage: 55.5 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9PCgjzmCwIij",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "outputId": "0b2fcfb4-87df-46a5-8b2f-b0fbea94de22"
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NOX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "      <th>PRICE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.613524</td>\n",
              "      <td>11.363636</td>\n",
              "      <td>11.136779</td>\n",
              "      <td>0.069170</td>\n",
              "      <td>0.554695</td>\n",
              "      <td>6.284634</td>\n",
              "      <td>68.574901</td>\n",
              "      <td>3.795043</td>\n",
              "      <td>9.549407</td>\n",
              "      <td>408.237154</td>\n",
              "      <td>18.455534</td>\n",
              "      <td>356.674032</td>\n",
              "      <td>12.653063</td>\n",
              "      <td>22.532806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.601545</td>\n",
              "      <td>23.322453</td>\n",
              "      <td>6.860353</td>\n",
              "      <td>0.253994</td>\n",
              "      <td>0.115878</td>\n",
              "      <td>0.702617</td>\n",
              "      <td>28.148861</td>\n",
              "      <td>2.105710</td>\n",
              "      <td>8.707259</td>\n",
              "      <td>168.537116</td>\n",
              "      <td>2.164946</td>\n",
              "      <td>91.294864</td>\n",
              "      <td>7.141062</td>\n",
              "      <td>9.197104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.006320</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.460000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.385000</td>\n",
              "      <td>3.561000</td>\n",
              "      <td>2.900000</td>\n",
              "      <td>1.129600</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>187.000000</td>\n",
              "      <td>12.600000</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>1.730000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.082045</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.190000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.449000</td>\n",
              "      <td>5.885500</td>\n",
              "      <td>45.025000</td>\n",
              "      <td>2.100175</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>279.000000</td>\n",
              "      <td>17.400000</td>\n",
              "      <td>375.377500</td>\n",
              "      <td>6.950000</td>\n",
              "      <td>17.025000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.256510</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.690000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.538000</td>\n",
              "      <td>6.208500</td>\n",
              "      <td>77.500000</td>\n",
              "      <td>3.207450</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>330.000000</td>\n",
              "      <td>19.050000</td>\n",
              "      <td>391.440000</td>\n",
              "      <td>11.360000</td>\n",
              "      <td>21.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.677083</td>\n",
              "      <td>12.500000</td>\n",
              "      <td>18.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.624000</td>\n",
              "      <td>6.623500</td>\n",
              "      <td>94.075000</td>\n",
              "      <td>5.188425</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>666.000000</td>\n",
              "      <td>20.200000</td>\n",
              "      <td>396.225000</td>\n",
              "      <td>16.955000</td>\n",
              "      <td>25.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>88.976200</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>27.740000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.871000</td>\n",
              "      <td>8.780000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>12.126500</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>711.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>396.900000</td>\n",
              "      <td>37.970000</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             CRIM          ZN       INDUS  ...           B       LSTAT       PRICE\n",
              "count  506.000000  506.000000  506.000000  ...  506.000000  506.000000  506.000000\n",
              "mean     3.613524   11.363636   11.136779  ...  356.674032   12.653063   22.532806\n",
              "std      8.601545   23.322453    6.860353  ...   91.294864    7.141062    9.197104\n",
              "min      0.006320    0.000000    0.460000  ...    0.320000    1.730000    5.000000\n",
              "25%      0.082045    0.000000    5.190000  ...  375.377500    6.950000   17.025000\n",
              "50%      0.256510    0.000000    9.690000  ...  391.440000   11.360000   21.200000\n",
              "75%      3.677083   12.500000   18.100000  ...  396.225000   16.955000   25.000000\n",
              "max     88.976200  100.000000   27.740000  ...  396.900000   37.970000   50.000000\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ROIdYEfJwOYI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import mean_squared_error"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TdPEIoeexVah",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mpgmym3cxXx-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X,y=data.iloc[:,:-1],data.iloc[:,-1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WJ5JqZ-7xuUS",
        "colab_type": "text"
      },
      "source": [
        "Now you will convert the dataset into an optimized data structure called **Dmatrix** that XGBoost supports and gives it acclaimed *performance and efficiency* gains. You will use this later in the tutorial."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NaOx9ewpxnH0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_dmatrix = xgb.DMatrix(data=X,label=y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "laIFh24azGcd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2, random_state=123)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ffnrr-9F0NEQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# xg_clf = xgb.XGBClassifier\n",
        "\n",
        "xg_reg = xgb.XGBRegressor( objective = 'reg:squarederror', colsample_bytree=0.3, \n",
        "                          learning_rate=0.1, max_depth=5, alpha=10, n_estimators=10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hIXELJCX0_gW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "xg_reg.fit(X_train,y_train)\n",
        "\n",
        "preds=xg_reg.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qhspFxeo1N9I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c64f2ffa-caa5-4d16-ae9c-faa79ba4ff63"
      },
      "source": [
        "rmse = np.sqrt(mean_squared_error(y_test, preds))\n",
        "print(\"RMSE: %f\" % (rmse))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE: 10.449300\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wDcSeBYM1c9j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4BwTnbwq8btI",
        "colab_type": "text"
      },
      "source": [
        "k-fold Cross Validation using XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s4paZWIR8gqr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "params = {\"objective\":\"reg:squarederror\", 'colsample_bytree': 0.3, 'learning_rate':0.1,\n",
        "          'max_depth':5, 'alpha':10}\n",
        "\n",
        "cv_results = xgb.cv( dtrain=data_dmatrix, params=params, nfold=3,\n",
        "                    num_boost_round=50, early_stopping_rounds=10, metrics=\"rmse\",\n",
        "                    as_pandas=True, seed=123)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lIp41lnq93P8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "24899824-9473-4e74-b086-706831f8b4db"
      },
      "source": [
        "cv_results.head(51)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>train-rmse-mean</th>\n",
              "      <th>train-rmse-std</th>\n",
              "      <th>test-rmse-mean</th>\n",
              "      <th>test-rmse-std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>21.679234</td>\n",
              "      <td>0.025626</td>\n",
              "      <td>21.677613</td>\n",
              "      <td>0.050617</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>19.772293</td>\n",
              "      <td>0.016054</td>\n",
              "      <td>19.773320</td>\n",
              "      <td>0.020571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>18.049563</td>\n",
              "      <td>0.058904</td>\n",
              "      <td>18.065856</td>\n",
              "      <td>0.082746</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>16.430113</td>\n",
              "      <td>0.013615</td>\n",
              "      <td>16.492199</td>\n",
              "      <td>0.020919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>15.025977</td>\n",
              "      <td>0.062132</td>\n",
              "      <td>15.132644</td>\n",
              "      <td>0.073104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>13.751878</td>\n",
              "      <td>0.071229</td>\n",
              "      <td>13.915182</td>\n",
              "      <td>0.105037</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>12.594594</td>\n",
              "      <td>0.050818</td>\n",
              "      <td>12.836291</td>\n",
              "      <td>0.092982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>11.577890</td>\n",
              "      <td>0.075426</td>\n",
              "      <td>11.879440</td>\n",
              "      <td>0.108604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>10.707665</td>\n",
              "      <td>0.095779</td>\n",
              "      <td>11.065584</td>\n",
              "      <td>0.111040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9.858511</td>\n",
              "      <td>0.084696</td>\n",
              "      <td>10.248752</td>\n",
              "      <td>0.055924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>9.128669</td>\n",
              "      <td>0.144228</td>\n",
              "      <td>9.579426</td>\n",
              "      <td>0.068845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>8.446362</td>\n",
              "      <td>0.102588</td>\n",
              "      <td>8.948772</td>\n",
              "      <td>0.068582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>7.836310</td>\n",
              "      <td>0.098086</td>\n",
              "      <td>8.387981</td>\n",
              "      <td>0.064135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>7.282486</td>\n",
              "      <td>0.067255</td>\n",
              "      <td>7.864691</td>\n",
              "      <td>0.112004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>6.803038</td>\n",
              "      <td>0.041624</td>\n",
              "      <td>7.445594</td>\n",
              "      <td>0.174268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>6.363438</td>\n",
              "      <td>0.084930</td>\n",
              "      <td>7.058918</td>\n",
              "      <td>0.165512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>5.984451</td>\n",
              "      <td>0.120797</td>\n",
              "      <td>6.737044</td>\n",
              "      <td>0.152061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>5.654591</td>\n",
              "      <td>0.120402</td>\n",
              "      <td>6.467680</td>\n",
              "      <td>0.137680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>5.352251</td>\n",
              "      <td>0.153629</td>\n",
              "      <td>6.236127</td>\n",
              "      <td>0.134963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>5.058460</td>\n",
              "      <td>0.133300</td>\n",
              "      <td>5.989471</td>\n",
              "      <td>0.179675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>4.820461</td>\n",
              "      <td>0.159472</td>\n",
              "      <td>5.826069</td>\n",
              "      <td>0.184797</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>4.639613</td>\n",
              "      <td>0.173275</td>\n",
              "      <td>5.675499</td>\n",
              "      <td>0.177443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>4.424060</td>\n",
              "      <td>0.180595</td>\n",
              "      <td>5.493176</td>\n",
              "      <td>0.207798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>4.259147</td>\n",
              "      <td>0.180153</td>\n",
              "      <td>5.362645</td>\n",
              "      <td>0.221873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>4.107167</td>\n",
              "      <td>0.184336</td>\n",
              "      <td>5.239616</td>\n",
              "      <td>0.231593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>3.950419</td>\n",
              "      <td>0.201744</td>\n",
              "      <td>5.130476</td>\n",
              "      <td>0.225683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>3.795581</td>\n",
              "      <td>0.188898</td>\n",
              "      <td>5.032134</td>\n",
              "      <td>0.262238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>3.659112</td>\n",
              "      <td>0.205095</td>\n",
              "      <td>4.940273</td>\n",
              "      <td>0.273222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>3.522903</td>\n",
              "      <td>0.202569</td>\n",
              "      <td>4.830635</td>\n",
              "      <td>0.305002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>3.428220</td>\n",
              "      <td>0.208392</td>\n",
              "      <td>4.768210</td>\n",
              "      <td>0.304069</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>3.336231</td>\n",
              "      <td>0.213666</td>\n",
              "      <td>4.727444</td>\n",
              "      <td>0.321256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>3.256712</td>\n",
              "      <td>0.198886</td>\n",
              "      <td>4.661351</td>\n",
              "      <td>0.317350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>3.140978</td>\n",
              "      <td>0.187456</td>\n",
              "      <td>4.585999</td>\n",
              "      <td>0.347965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>3.052053</td>\n",
              "      <td>0.187743</td>\n",
              "      <td>4.518073</td>\n",
              "      <td>0.377065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2.968510</td>\n",
              "      <td>0.170318</td>\n",
              "      <td>4.442965</td>\n",
              "      <td>0.395139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>2.890633</td>\n",
              "      <td>0.174692</td>\n",
              "      <td>4.376086</td>\n",
              "      <td>0.416283</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2.792710</td>\n",
              "      <td>0.158808</td>\n",
              "      <td>4.298692</td>\n",
              "      <td>0.405610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>2.731792</td>\n",
              "      <td>0.147543</td>\n",
              "      <td>4.255738</td>\n",
              "      <td>0.427538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>2.686857</td>\n",
              "      <td>0.150795</td>\n",
              "      <td>4.237689</td>\n",
              "      <td>0.432736</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>2.623845</td>\n",
              "      <td>0.137448</td>\n",
              "      <td>4.191464</td>\n",
              "      <td>0.420895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>2.570019</td>\n",
              "      <td>0.128957</td>\n",
              "      <td>4.153117</td>\n",
              "      <td>0.412739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>2.509184</td>\n",
              "      <td>0.124956</td>\n",
              "      <td>4.100206</td>\n",
              "      <td>0.405215</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>2.458303</td>\n",
              "      <td>0.112624</td>\n",
              "      <td>4.069338</td>\n",
              "      <td>0.411593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>2.428883</td>\n",
              "      <td>0.109376</td>\n",
              "      <td>4.054069</td>\n",
              "      <td>0.410480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>2.398084</td>\n",
              "      <td>0.115625</td>\n",
              "      <td>4.042696</td>\n",
              "      <td>0.411912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>2.367925</td>\n",
              "      <td>0.109945</td>\n",
              "      <td>4.028212</td>\n",
              "      <td>0.410623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>2.333494</td>\n",
              "      <td>0.109729</td>\n",
              "      <td>4.008792</td>\n",
              "      <td>0.426057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>2.312211</td>\n",
              "      <td>0.110639</td>\n",
              "      <td>3.998730</td>\n",
              "      <td>0.431785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>2.290414</td>\n",
              "      <td>0.113872</td>\n",
              "      <td>3.987814</td>\n",
              "      <td>0.427569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>2.266126</td>\n",
              "      <td>0.105188</td>\n",
              "      <td>3.975679</td>\n",
              "      <td>0.419003</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    train-rmse-mean  train-rmse-std  test-rmse-mean  test-rmse-std\n",
              "0         21.679234        0.025626       21.677613       0.050617\n",
              "1         19.772293        0.016054       19.773320       0.020571\n",
              "2         18.049563        0.058904       18.065856       0.082746\n",
              "3         16.430113        0.013615       16.492199       0.020919\n",
              "4         15.025977        0.062132       15.132644       0.073104\n",
              "5         13.751878        0.071229       13.915182       0.105037\n",
              "6         12.594594        0.050818       12.836291       0.092982\n",
              "7         11.577890        0.075426       11.879440       0.108604\n",
              "8         10.707665        0.095779       11.065584       0.111040\n",
              "9          9.858511        0.084696       10.248752       0.055924\n",
              "10         9.128669        0.144228        9.579426       0.068845\n",
              "11         8.446362        0.102588        8.948772       0.068582\n",
              "12         7.836310        0.098086        8.387981       0.064135\n",
              "13         7.282486        0.067255        7.864691       0.112004\n",
              "14         6.803038        0.041624        7.445594       0.174268\n",
              "15         6.363438        0.084930        7.058918       0.165512\n",
              "16         5.984451        0.120797        6.737044       0.152061\n",
              "17         5.654591        0.120402        6.467680       0.137680\n",
              "18         5.352251        0.153629        6.236127       0.134963\n",
              "19         5.058460        0.133300        5.989471       0.179675\n",
              "20         4.820461        0.159472        5.826069       0.184797\n",
              "21         4.639613        0.173275        5.675499       0.177443\n",
              "22         4.424060        0.180595        5.493176       0.207798\n",
              "23         4.259147        0.180153        5.362645       0.221873\n",
              "24         4.107167        0.184336        5.239616       0.231593\n",
              "25         3.950419        0.201744        5.130476       0.225683\n",
              "26         3.795581        0.188898        5.032134       0.262238\n",
              "27         3.659112        0.205095        4.940273       0.273222\n",
              "28         3.522903        0.202569        4.830635       0.305002\n",
              "29         3.428220        0.208392        4.768210       0.304069\n",
              "30         3.336231        0.213666        4.727444       0.321256\n",
              "31         3.256712        0.198886        4.661351       0.317350\n",
              "32         3.140978        0.187456        4.585999       0.347965\n",
              "33         3.052053        0.187743        4.518073       0.377065\n",
              "34         2.968510        0.170318        4.442965       0.395139\n",
              "35         2.890633        0.174692        4.376086       0.416283\n",
              "36         2.792710        0.158808        4.298692       0.405610\n",
              "37         2.731792        0.147543        4.255738       0.427538\n",
              "38         2.686857        0.150795        4.237689       0.432736\n",
              "39         2.623845        0.137448        4.191464       0.420895\n",
              "40         2.570019        0.128957        4.153117       0.412739\n",
              "41         2.509184        0.124956        4.100206       0.405215\n",
              "42         2.458303        0.112624        4.069338       0.411593\n",
              "43         2.428883        0.109376        4.054069       0.410480\n",
              "44         2.398084        0.115625        4.042696       0.411912\n",
              "45         2.367925        0.109945        4.028212       0.410623\n",
              "46         2.333494        0.109729        4.008792       0.426057\n",
              "47         2.312211        0.110639        3.998730       0.431785\n",
              "48         2.290414        0.113872        3.987814       0.427569\n",
              "49         2.266126        0.105188        3.975679       0.419003"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yA0MDZLU-GNa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "0fbbef06-ad28-40ff-c055-660f43dd8db8"
      },
      "source": [
        "print((cv_results[\"test-rmse-mean\"]).tail(1))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "49    3.975679\n",
            "Name: test-rmse-mean, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "asX6YMzV-dIj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}