{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Nearest Neighbors regression\n\nDemonstrate the resolution of a regression problem\nusing a k-Nearest Neighbor and the interpolation of the\ntarget using both barycenter and constant weights.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Generate sample data\nHere we generate a few data points to use to train the model. We also generate\ndata in the whole range of the training data to visualize how the model would\nreact in that whole region.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn import neighbors\n\nrng = np.random.RandomState(0)\nX_train = np.sort(5 * rng.rand(40, 1), axis=0)\nX_test = np.linspace(0, 5, 500)[:, np.newaxis]\ny = np.sin(X_train).ravel()\n\n# Add noise to targets\ny[::5] += 1 * (0.5 - np.random.rand(8))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Fit regression model\nHere we train a model and visualize how `uniform` and `distance`\nweights in prediction effect predicted values.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "n_neighbors = 5\n\nfor i, weights in enumerate([\"uniform\", \"distance\"]):\n    knn = neighbors.KNeighborsRegressor(n_neighbors, weights=weights)\n    y_ = knn.fit(X_train, y).predict(X_test)\n\n    plt.subplot(2, 1, i + 1)\n    plt.scatter(X_train, y, color=\"darkorange\", label=\"data\")\n    plt.plot(X_test, y_, color=\"navy\", label=\"prediction\")\n    plt.axis(\"tight\")\n    plt.legend()\n    plt.title(\"KNeighborsRegressor (k = %i, weights = '%s')\" % (n_neighbors, weights))\n\nplt.tight_layout()\nplt.show()"
      ]
    }
  ],
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      "display_name": "Python 3",
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      "name": "python3"
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    "language_info": {
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      "file_extension": ".py",
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