{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Plot individual and voting regression predictions\n\n.. currentmodule:: sklearn\n\nA voting regressor is an ensemble meta-estimator that fits several base\nregressors, each on the whole dataset. Then it averages the individual\npredictions to form a final prediction.\nWe will use three different regressors to predict the data:\n:class:`~ensemble.GradientBoostingRegressor`,\n:class:`~ensemble.RandomForestRegressor`, and\n:class:`~linear_model.LinearRegression`).\nThen the above 3 regressors will be used for the\n:class:`~ensemble.VotingRegressor`.\n\nFinally, we will plot the predictions made by all models for comparison.\n\nWe will work with the diabetes dataset which consists of 10 features\ncollected from a cohort of diabetes patients. The target is a quantitative\nmeasure of disease progression one year after baseline.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\n\nfrom sklearn.datasets import load_diabetes\nfrom sklearn.ensemble import (\n    GradientBoostingRegressor,\n    RandomForestRegressor,\n    VotingRegressor,\n)\nfrom sklearn.linear_model import LinearRegression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Training classifiers\n\nFirst, we will load the diabetes dataset and initiate a gradient boosting\nregressor, a random forest regressor and a linear regression. Next, we will\nuse the 3 regressors to build the voting regressor:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "X, y = load_diabetes(return_X_y=True)\n\n# Train classifiers\nreg1 = GradientBoostingRegressor(random_state=1)\nreg2 = RandomForestRegressor(random_state=1)\nreg3 = LinearRegression()\n\nreg1.fit(X, y)\nreg2.fit(X, y)\nreg3.fit(X, y)\n\nereg = VotingRegressor([(\"gb\", reg1), (\"rf\", reg2), (\"lr\", reg3)])\nereg.fit(X, y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Making predictions\n\nNow we will use each of the regressors to make the 20 first predictions.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "xt = X[:20]\n\npred1 = reg1.predict(xt)\npred2 = reg2.predict(xt)\npred3 = reg3.predict(xt)\npred4 = ereg.predict(xt)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Plot the results\n\nFinally, we will visualize the 20 predictions. The red stars show the average\nprediction made by :class:`~ensemble.VotingRegressor`.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "plt.figure()\nplt.plot(pred1, \"gd\", label=\"GradientBoostingRegressor\")\nplt.plot(pred2, \"b^\", label=\"RandomForestRegressor\")\nplt.plot(pred3, \"ys\", label=\"LinearRegression\")\nplt.plot(pred4, \"r*\", ms=10, label=\"VotingRegressor\")\n\nplt.tick_params(axis=\"x\", which=\"both\", bottom=False, top=False, labelbottom=False)\nplt.ylabel(\"predicted\")\nplt.xlabel(\"training samples\")\nplt.legend(loc=\"best\")\nplt.title(\"Regressor predictions and their average\")\n\nplt.show()"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.21"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}