kNN_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "00000-f78c4176-6d41-4d8e-b623-451927d02d17",
    "deepnote_cell_type": "markdown",
    "tags": []
   },
   "source": [
    "Load NumPy, Matplotlib and Scikit-learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cell_id": "00000-6f3e5e84-0f88-401a-ae30-4b75b905593a",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 1573,
    "execution_start": 1613311005701,
    "source_hash": "b74692eb",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import KNeighborsRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "00002-5316e25e-1de2-46d7-9840-fa09841c8deb",
    "deepnote_cell_type": "markdown",
    "tags": []
   },
   "source": [
    "Define training data and neigborhood size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "cell_id": "00001-902c6f67-1f07-45f0-a5a5-ffd924cec8ed",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 2,
    "execution_start": 1613311027972,
    "source_hash": "ff9dae94",
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = [[0], [1], [2], [3], [4]]\n",
    "y = [0, 0.3, 0.75, 1, 2]\n",
    "k = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "00004-c64a9b1d-f098-4919-93de-9a4f58f32f3f",
    "deepnote_cell_type": "markdown",
    "tags": []
   },
   "source": [
    "Fit the kNN model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "cell_id": "00002-965694a9-8960-4eaf-b385-967d89a253dc",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 9,
    "execution_start": 1613311029004,
    "source_hash": "2b1be20b",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(n_neighbors=2)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neigh = KNeighborsRegressor(n_neighbors=k)\n",
    "neigh.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "00006-d937f970-d833-4118-92d6-f59ba66dd55d",
    "deepnote_cell_type": "markdown",
    "tags": []
   },
   "source": [
    "Evaluate and plot predictor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "cell_id": "00003-e2526324-7b11-4d7b-b340-e6e5ced84c18",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 185,
    "execution_start": 1613311030615,
    "source_hash": "6e21faf0",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_eval = np.linspace(0,4,1000)\n",
    "X_eval = X_eval.reshape(-1,1)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(X_eval,neigh.predict(X_eval), label=\"kNN regression predictor\")\n",
    "plt.plot(X,y, 'rs', markersize=12, label=\"trainin set\")\n",
    "plt.title(\"Simplistic test of kNN regression\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "deepnote": {},
  "deepnote_execution_queue": [],
  "deepnote_notebook_id": "de759d3f-384e-4796-8c7b-a891385e2084",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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