k-nearest-neighbour.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "f64dea5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "using Plots, NearestNeighborModels, MLJBase, CategoricalArrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "197a7d63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RcmJsvKx797aVO4vqS5T38wIDA4koPT3904sxDINLWQGgFEdHx169eqlqbcOHD/juu5737t3Lz89v3Hh8Jf4KhkqbPPmnY8d6xsVl5uUNJrLk8S7Z2S3bu3dbJVbVq1f3+PikJUtaFBe3l0rNBILz7drVXbt2haojf54qi/fNmzfm5uY8Hq9r166rVq2ytbX94GIymUwoFL7bduRwOB4eHqhPACg/gUDQvHlztlPoIz6ff/36qR079uzd+2denqhly6YzZkRYW1tXbm2TJ/84fHj/W7duFRQU+PkNql69umrTlhOHYZjPL/XW33//vXfv3g/uHc3JycnMzPTy8kpOTh48eLCNjc3+/fs/uJKBAweGhYW9+z+Oz+cfOHCgRo0aUqlUoVDw+fyK/1eAWsnlcolE8oVX0YIaMAwjFotNTU3ZDgKfl5+fj/Ndq4Kpqelnz8xS2RahtbW1stucnZ3nz5/fvn37j+0mrV+/vpub28KFC0vNUYTaAkWoLRiG4XK5AoGA7SDweQzDoAjZUiWXT4hEImNjY+ztBAAAzVfeLcKkpKRr167FxsZmZGQcOHCgevXqzZo1I6LmzZuvXbu2adOmBw4cMDAwqF279suXL6dMmTJ48OCqjA0AAKAaFSjCAwcOEFH9+vUPHDgQGBioLEI3NzflzkxjY+P169enpKTY2dn99NNPP/30U9WFBgAlsVj833//paenN2jQwMPDg+04AFqpYifLqMSiRYtEIhGOEWovHCPUEMePn/rppznFxc2Ki6sZG0f7+VXbs2etpeX/v1UHwzCFhYU4RqgVRCKRubk52yn0FJ4+AaCVHjx4MHz4gqys88obb4pEFB6+99tvR1y4cIDtaABaBvcaBdBKixdvyMr64+3tp4mIZLL+Dx4IU1JSWEwFoI1QhABa6fHjF0QNSg1lsgbPnz9nJQ+A9kIRAmglOzsborRSQx4v9WN3dAKAj0ERAmilkSN7W1quen8WLxDEe3vj+ewAFYMiBNBKvXv37NFDYGv7LdFZohgjozXOzn0OHfobN7IAqCicNQqgrbZvX3n9+vWdO4+npma1bNlo5MhI3KMLoBJQhABarFmzZspbWwBApWHXKIA2EYlE48f/Urfu1zVrBn733Y+JiYlsJ6qwyMgrrVv38/Dw9/fv9u+/+9R/Tw+AUrBFCKA10tLS/P27pqaOk0hmERm+fBlx8WL3kyc3fPWVH9vRyuvXX/9cvToqJ2cRkffr10ljxy7et+/kiRM72M4Feg1bhABaY/Lk+YmJcySSIUSmRIYM0zEj48CwYVPZzlVeycnJ69cfyck5TFSPiEPkmpu7+upVzsWLF9mOBnoNRQigNSIjoxWK4PdnNTMyJGKxmJ1AFRQZGZmf34PIoOQwJyfk4MFwtiIBEIoQQIsoFFSqRYiIwzGWSCRsxKmw4uJiqdS0zNi0oKCIhTQAb6EIAbRGzZoeRHffn4kMDYUlnzihyRo3bmxlFVlqaGp6qXXrpqzkAVBCEQJojeXLZ9nZ/Uj07m6iWVZWg+fNm8xmpopo0qRJgwYKPn8NkUI54XJPOjmdDAnpx24w0HM4axRAa/j4+Jw8uXL48FEZGVIOh29iIvrzzxl9+vRiO1cFnDr177Rp8/bta8rlujNMSrNmDTZsOI6nkAK78GBeqDA8mJd1YrFYIpF8do+oxj6Yl2GYlJQUe3t7Hg//Fv8fPJiXRfhdCKB9TExMtPofIhwOx9nZme0UAP+DY4QAAKDXUIQAAHoqNja2WbNuLi5fubr6ffvtiOTkZLYTsQO7RgEA9FFY2JkhQxZkZ28kqktEx46du3696/Xrx9zc3NiOpm7YIgQA0Efjxs3Nzj6sbEEiUig6pKYunjx5AbupWIEiBADQOzk5OQUFZkTVSg4Zpt3Nm7FsRWIRihAAQO/cu3cvKyu3zJiFC+o0AYoQAEC/MAwzfPh0hUJOlFNyzuFc9vVtzFYqFqEIAQD0S1xcXEFBDaI/iPoQvXw7jjI0/H7p0tlsJmMJzhoFANAv2dnZcrkDUXciG6KRREIiDpF7rVqunp6ebKdjAYoQAEC/eHp6crkPiIioJdH5t+OIpk2PsJaJVdg1CgCgX5ycnBo1sjU03FNilmNrO2fGjB9Yy8QqbBECAOidw4c3hYSMuXVrW1FRIJ+fwedfX7duXoMGDdjOxQ4UIQCA3rGwsDh5cufr16//++8/e3v7Ro2WGhsbsx2KNShCAAA95e7u7u7uznYK9uEYIQAA6DUUIQAA6DUUIQBA5YnFYoVCwXYK+CIoQgCAytixY4+Hh7+HRwdn52bffPPt8+fP2U4ElYSTZQAAKmzhwpVLlsTk5p4jsiKitLQbrVr1u3XruKurK9vRoMKwRQgAUDESiWTlyi25uZuVLUhERAFpab/9+utyNmNBZaEIAQAqJj4+nqghkWHJIcO0u3btNluR4EugCAEAKobH4xEVlxkX8Xg42KSVUIQAABXj6enJ4z0nEpYcGhkd6tYtiK1I8CVQhAAAFcPlclet+t3WNpjoDhERSfj8jdWrb5o5czzLyaBSsCEPAFBhvXt39/LymDRp4dOnL4yMjHr27PD77xECgYDtXFAZKEIAgMpo1KjR+fN72U6herm5uQ8fPuTz+fXq1TMxMWE7jjpg1ygAABARKRSKKVN+9/Jq3737vk6dNnh6tti8+V+2Q6kDtggBAICIaPbsRX//LS4oiCYyICKigqlT+zs62nbt2pnlZFUMW4QAAEAMw2zdur+gYP7bFiQiQU7OmrlzV7EZSy1QhAAAQEKhkMihzG5C97S0THYCqRF2jQIAaASxWPzo0SOFQlGvXj31n4AqEAgUCmGZcZGRkcEHltYt2CIEAGDfP//sqFGjRadOG7p02eLp+fWCBeq+bamRkVGdOi4czrWSQz5/U9++XdWcRP2wRQgAwLKdO/fOn38lN/cqkfJyBcmSJWMFgr9//nm0OmPs2rXym296p6X1F4vbE0ksLffUrv3wt9+OqDMDK7BFCADAsvnz1+bmrn3bgkRklJe3YtmyTWqO4ebmFhcXtWCBSXDwij59Nv/zT+CNG6f14VJCbBECALAsL09MZPv+zLS4mCeXyw0M1HqIjs/nT5z408SJ6vxM9mGLEACAZVyugkhRZlyk5hbUWyhCAACWtW//NY+3r+SEwznj79+YrTz6BrtGAeAzjh0L+/ffk5mZOYGBDadM+dHGxobtRLpmxYrfbtwITkx8kp/fk4hnahrm7Hx006ZjbOfSFyhCAPgouVzepcvAW7cscnJGENlERV3bsqXt0aN/N2sWwHY0nWJlZRUdHbZ//9GTJ9fIZPJOnQJHj75iaGjIdi59gSIEgI/atm3XtWvV8/OXKF/KZDXT0r4JCekTH3+Dw+Gwm03HGBgYjBo1dNSooWwH0Uc4RggAH7V9+/H8/FHvz9wKCz2eP3+u2g+SSqULF67w8mrh6vpVkyYdTp48rdr1A3wCtggB4KOEwtwyp/WTQmEnFJa9F1flKRSK5s2DHz9uU1AQTmSalJQ0cOCkn366u2DBTBV+CsDHYIsQAD6qYcM6RDGlhhzOvVq1aqnwUw4cOPTkSaOCghlEpkRE5CIU7tmw4Vh6eroKPwXgY1CEAPBRv/wyxs7uF6KUtwOGz1/Rpk0Da2trFX7KsWORIlHP92fcoqLO169fV+GnAHwMdo0CwEd5e3vv2/d/w4f3EIu95HJbLvdmt27N161T8QPqZDI5UekzJBUKQ6lUqtoPAvggFCEAfEpQUOvnz6+9ePEiKyurXr0/rKysVP4R7dt/dfJkeGGhf8mhQHD+q68GqvyzAMrCrlEA+Awej1enTp3mzZtXRQsS0eDBoS4uYSVurSIWCKZ17Fjfzc2tKj4OoBQUIQCwjM/n37hxKiTkqqOjj719C1fXVn/84bFt2wq2c4G+wK5RAGCftbX1jh2riEgikRgZGbEdB/QLtggBQIOUbMGioqLff//T17dLgwZBP/wwLSMjg8VgoMOwRQgAmig7O9vfv0tycqhYvI3IOC4u/NixDufObW/UqBHb0UDXYIsQADTRjBmLXr/+WSweT2RPZCGX905L2zNo0CS2c4EOQhECgCY6d+6yTNb7/Vnd1FSxWCxmJxDoLhQhAGgiuVxR9ip7DkeAIgSVQxECgCby8HAjevD+TMzlplf67m5paWnnzp27fPlyXl7el8cDXYIiBABN9Ndf021tfyRKfDvIt7QcNWPGT5V4DqJcLv/ppxkNG37br9+lXr2Oe3m1Wb78b9WmBa2Gs0YBQBMFBATs3z9vxIg+hYVWRAIu98Vvv/1cuefWTps2b/t2fmFhFJGyRIv/+GNQ9eoOffr0Umlk0FbYIgQAjSMSiUaNmhIaOrGwUCCXJ3bo4PDs2dXKtSDDMLt2HSksnPu2BYmILxSumDdvnerygnbDFiEAaBaGYb755tuHD0Mlkj+JOESKffs2Pn3aNzr6ZCX2i+bm5hJVIzJ4f+yclaXKZwuDVsMWIQBolvDw8Jcva0skw95uw3Elkh+ePXOOjIysxNrMzMwUirKdJzYyMvjA0qCXUIQAoFmiomKFwjalhtnZQdeuxVRibTwer1GjmhzOhZJDY+P1ISHdKx8RdAt2jQKAZjE2NuRwihjmvSGHU8Tnl76ssJx27lzx9dffpqREFRR0IhJbWe1r0CB5zpx9n38n6AdsEQKAZunUqY2NzaFSQ1vbQx07BlVuhU5OTo8fX1m92rNfvx3ff39yx47gK1eOGRsbf3FS0BHYIgQAzeLj49Ohg9OpU6Nzc38jciRKtrSc061b7fr161d6nTweb9iwQcOGDVJdTNAdKEIA0Di7d6/bs+fAkiVDMjOz7O2rzZw5Ctf8QdVBEQKAJgoJ6RsS0pftFKAXynuMMCYmZunSpaNHjz5x4sTHlgkLCwsODu7UqdPevXtVFA8AAKBqlXeLcNu2bYWFhbdv33Z0dOzWrVvZBW7evDlw4MBNmzYJBIIhQ4ZYWVl16tRJpVEBAABUr7xFuHr1aiIKDQ392AJr1qwZNWpUnz59iGjatGmrV69GEQIAgOZT2eUTd+7cCQwMVP7crFmz2NhYVa0ZtJpEIrl79+61a9eEQtzRCgA0kcpOlklLS3v3nDBbW9v09HSFQsHlfqBo4+Lizp49e+bMGeVLDoezfft2Dw8PqVSqUCikUqmqIkFFJSQk5OTkeHl5mZqafmIxuVwukUjkcvlnV3j84ME/Z83ykUpNFYobXG7r3r1nL15sYIBbW6kJwzBisZgpdWk6aKSCgoJK3EkVPsvU1PSDTVSSyorQzMzs3ZOjCwoKzM3NP/bZnp6eXbp0GTNmjPIlh8OpV68el8tVFiGfz1dVJCi/G9evTxw0yFEkslMoYhmmTa9e81ev/th3oSxCExOTT68z8vLlbZMnX8rOtiQiIgXRHzt3LubxFq5Zo+r48GEMw3C5XIFAwHYQ+DyGYczMzNhOoadUVoTu7u7x8fHKn+Pj493c3D62pJGRkaOjo6+vr6o+Gr5QfHz82O7dD2ZkuBMREUO0YufOcTk5Gw4c+JLVrpo7d/nbFiQiLtHcwkLfAwd+X77c0LCS98oCAFC5LzpGmJycvHTpUuXP/fv337p1a3FxsVwu37hxY//+/VURD9Rh3aJFv2Zmur99ySGaWFT0+PLlzMzML1nt61evSt0IhEvkyeGkpKR8yWoBAFSrvEU4a9YsGxubQ4cOLV682MbGZvPmzUT06tWrKVOmKBcYOnSoi4tLrVq1ateuLZPJxo0bV1WRQdXi7t71K3MYyUehePLkyZes1tTUNLfMMJNhLC0tP7A0AABLyrtrdOHChQsXLiw1DAwMfHduC5/PP3bsWGJiokwm8/DwUGFEXcIwzOvXrxMTE2vVquXo6Mh2nP8xt7DIISqVJpvLtbCw+JLV9hgw4O+FC6e/PXJMRHeJjF1cUIQAoFG+aNcoh8Ph8d6rUldXV7Tgxzx+/Lht48YTAwIO9ugxsHHj79q1S09PZzsUEVHwoAAw6zgAACAASURBVEGbzc1LTlKJHvH5X3KPYyIaM3XqVR+fcVZWt4geEq3h84e7uq7Zh2ffAIBmwb1G1SQ3Nze0bdt/U1LedcvpiIjv2rWLuHeP9XOmQwYPPrFnz+ibN0cLhfZEVw0M/s/WdtmOHZ895/jT+Hz+8aioo4cObdm7V1xQ4NO6ddTYsZ++MAMAQP1QhGqyZ8eOoVlZJbewOjPMwaSk6Ojo5s2bsxaLiIi4XO7es2ePHjq0ZseOjLS0RgEBp2bNcnBwUMnKe/bu3bN3b5WsCgCgKqAI1eRpTEwviaTU0E8ojHv8mPUiVEJjAYB+whPq1cTCzi6rzDDDxMTSyoqFNAAA8BaKUE26hYRssrUteY1CIdExgSAoKIi1TAAAgCJUG19fX7/Bg7vZ2l4mek10gsNpV63a1CVL3t2gFQAAWIFjhOrz27JlN/v337N27Zv4+NqNG++aPLlGjRpshwIA0HcoQrXy9/f39/dnOwUAAPx/2DUKAAB6DUX4KQqFYvO6dR0aN/Zzc+vdqlXk5ctsJwIAABXDrtGPYhimT9u2dWJidotEdkRxb95M7dUrdvLkn2fPZjsa6IuCgoIzZ868fvrUvXbtTp064cmCAFUBW4QfFXbihPOdO4tEIjsiIqpLdCQnZ/fKlV/4cCKAcrp88eLXtWs/HDLEcdash0OGtKpd+1JEBNuhAHQQtgg/KuLQob657z1HiEfUVSy+evVqjx492EoFeiInJ+fnkJBzaWnVlK8LCn4sKOgQGhrx+DEuuQFQLWwRfpSkqMi4zNBYLi8uLmYhDeiZsOPHQ3Nzq5WYVCMakJt74tgx1jIB6CgU4Uc1bdPmkolJqeElMzMfHx9W8oBeSXrxokZRUalhzaKixOfPWckDoMNQhB81YMiQQ46Op94+ikhGtNDExLF581q1arEbDPSBS82aL41L75KINzZ2xW8/AFVDEX6UiYnJiejoA8HBfvb2nRwc/BwcaOLEf/bvZzsX6IXg7t13W1pmlJhkEP1rYdENx6cBVA0ny3yKg4PD1mPHFApFTk6Ora0t23FAj1hbWy/fvbvToEE9hcI6hYVPTU0PW1kt274dZ8oAqByK8PO4XC5aENSvdVBQ5NOnp0+ffvn4cV1v7yudO+M6QoCqgCIE0FwCgaBPnz5spwDQcThGCAAAeg1FCAAAeg1FCAAAeg1FCAAAeg1FCAAAeg1FCAAAeg1FCAAAeg1FCAAAeg1FCAAAeg1FCAAAeg1FCAAAeg1FCAAAeg1FCAAAeg1FCAAAeg1FCAAAeg1FCAAAeg1FCAAAeg1FCAAAeg1FCAAAeg1FCAAAeg1FCAAAeg1FCAAAeg1FCAAAeg1FCKBTkpKSZo4Z0z0gYGTPnmHHjrEdB0AL8NgOAAAqE37mzKzBg2dlZo5jmFSidZGRewICdp06xeFw2I4GoLmwRQigI2Qy2bTvvz+TkdGLYZyJfIg25eTYXLt2+MABtqMBaDQUIYCOuHv3rp9EYvv+cEReXtiOHewEAtASKEIAHZGXl2ctl5ca2hDl5uSwkgdAW6AIAXRE3bp1b/NKH/W/yeHU/+orVvIAaAsUIfvy8/Ojo6MvXbqUg3+5wxdwdnZ28fdfZ2TEvJ28IFpgZ/fDlClsxgLQeDhrlGUbV636e+HC5jKZsUIxjcfrMmjQ3D//5HLxDxSojH/27581ZkxAWJgPUbqBQbql5epNm1xdXdnOBaDRUIRsOrRvX/ivv14VCo2JiEhONGPDhj8Fgul//MFyMtBOpqamK7ZuFYlET548sbe3r169emFhIduhADQdtjzYtHbevFVvW5CIDIgW5ufv2biRzUyg/czNzf38/Nzc3NgOAqAdUIRsysvJcXx/YkhkpVDk5+ezEwgAQP+gCNnENTSUlBnmMYypqSkLaQAA9BKKkE0de/bcwueXnJzncOo0aYKTZQAA1AYny7BpxoIFva9effrsWb/cXCOiUwJBmKPjke3b2c4FAKBHUIRsEggEp2/ePHzgwL9HjkiLi5t17Hhl+HBDQ0O2cwEA6BEUIcs4HE7vfv169+vHdhAAAD2FY1EAAKDXUIQAAKDXUIQAAKDXUIQAAKDXUISahWGYHZs2dWjc2M/NrVtAQPi5c2wnAgDQcThrVLOEdu7sEh29Ky+vGlH8mzcz+ve/PmLEnCVL2M4FAKCzsEWoQS5cuGBy8+ZfeXnViIjIk2hvTs65rVsTExNZTgYAoLtQhBok4ujRPu8/m5dL1EMkioyMZCsSAIDOQxFqEGlRkXGZobFMVlxUxEIaAAD9oK1FmJ6ePmHIkMCaNQM9PccOGJCamsp2IhVo2rr1RTOzUsOL1tZ+X33FSh4AAH2glUX4/Pnzjo0bt921KzI+PvLly85793Zu0iQuLo7tXF+qT79+F5ydDxoYKF/KiZYaGxv6+DRs2JDdYAAAOkwri3DWqFF/p6Z2l8sNiQyJuioUW9LSZowYwXauL2VoaBgWHX2hb18fe/sODg5+jo4F48ZtO3aM7VwAALpMKy+fePboUcD7k6ZECc+esZNGpWxsbNbv2cMwjFAotLa2ZjsOAIDu08otQoMPDblEDMOoO0rV4HA4aEEAAPXQ9CLMzs6+detWqXNhLO3tX7+/WDKRwNaWw+GoMxsAAOgAzd01mpGRMWHQoKTY2PoM85LLJXf3NXv21KxZk4jmrlw5uE+f7dnZHkRE9IZosI3NnBUrWM2rO4qKik6cOPH0zh2XWrW6BAfb29uznQgAoAppaBEqFIq+QUHTHj7s8nZv5+309H5t2kQ+fiwQCL5p02bB0aNDf/hBmpXF5XC41ta/r13bOiiI3cy6ITYm5odevbpmZTUuLHxjaNjV2nriokWhw4eznQsAoKpoaBFGRUXVTk7uUuKYnx9R34yMA3v3Dv3+eyJq2arVpUePiouLGYYxNi57GTr7GIYJDw+PuXLF1MKiTceOjRo1YjvR50kkkhE9ex5JTHRXvpZKR6ant5s+3a9ly9q1a7ObDQCgimjoMcLHDx9+9f7NxojIv6jo0fXrJSd8Pl8zWzArK6uDr+/R776rPX++zfTpM9u2HT94sEKhYDvXZ0RFRX1dUOBeYmJCNDEra9+WLaxlAgCoYhpahBZWVtl8fqlhNodjYWfHSp6KGj9w4Pj799cJhb2JBjHMycxM3tGjW9avZzvXZ6SkpLgXFJQaujNM8osXrOQBAFADDS3CoKCgQ+bmxSUmDNEWG5vg775jLVO5FRUVPYuN7SaXlxzOFol2a3wRurq6vihzj7fnXK5b3bqs5AEAUAMNLUIHB4cf5s7tYGcXTpRCdI2oh62t/+DBTZo0Kc/bc3Jybt++nZaWVtU5P/bpDmUu5LAlysvNZSVP+bVo0eKGhcXjEpM8ouU2NiHff89aJgCAKqahJ8sQ0fdjxzZv23bD4sWrHj1yq1Fj1s8/NwsM/Oy7MjMzJw4Z8vrWrf9ddOHmtnbvXuVFF2pja2ubXOZwYAqRja2tOmNUAo/H23Hq1PBu3QKyspoKhQkCwVELi99Wr/bw8GA7GgBAVdHcIiQib2/v5du2lX95hmH6tW078cGDbm97qORFF1US8UOMjIz8WrfeduzYUIlEOVEQzbS0HD55stoyVJq3t3fUkycRERFxDx408PCY0LathYUF26EAAKpQBYowMzPz1KlTHA4nODi47A3A8vLybt68+e6lt7e3i4uLajKW27Vr12okJnYrsTXmR/RdRsb+3buHjRypziTLtm4d3rPnibt3OwiF+TzeEXPzzsOH9x80SJ0ZKs3AwKB9+/bt27dnOwgAgDqUtwgTEhL8/f3btWsnk8lmzZp169YtR0fHkgs8e/ase/fuLVq0UL6cNGmS+ovw8aNHXwmFpYb+RUUnb9wg9RahQCDYFx5+7969m9HRLlZWe1u2dHV1VWcAAAAop/IW4fLlyzt37rx161Yi6t+//5o1a+bPn19qGUdHx/DwcBUHrAhzC4t4Pp/E4pLDbCJzlg7ONW7cuHH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      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = 30 \n",
    "\n",
    "x1 = vcat(rand(n), rand(n) .+ 0.90);\n",
    "x2 = vcat(rand(n), rand(n) .+ 0.90);\n",
    "\n",
    "cols = vcat([:red for i in 1:n], [:blue for i in 1:n])\n",
    "\n",
    "scatter(x1, x2, color = cols, legend = false)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "7e2064ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60×3 Matrix{Any}:\n",
       " 0.466567   0.550706   \"A\"\n",
       " 0.928711   0.0225159  \"A\"\n",
       " 0.259835   0.020969   \"A\"\n",
       " 0.99126    0.716147   \"A\"\n",
       " 0.327618   0.357313   \"A\"\n",
       " 0.0898824  0.517429   \"A\"\n",
       " 0.782444   0.115977   \"A\"\n",
       " 0.454761   0.978519   \"A\"\n",
       " 0.314542   0.610891   \"A\"\n",
       " 0.664155   0.203233   \"A\"\n",
       " 0.668492   0.407395   \"A\"\n",
       " 0.104117   0.161101   \"A\"\n",
       " 0.0264979  0.554545   \"A\"\n",
       " ⋮                     \n",
       " 1.4174     1.26384    \"B\"\n",
       " 1.37435    1.70081    \"B\"\n",
       " 0.917531   1.73676    \"B\"\n",
       " 1.23195    1.09281    \"B\"\n",
       " 0.968574   1.45601    \"B\"\n",
       " 1.31925    1.06382    \"B\"\n",
       " 1.26027    1.53186    \"B\"\n",
       " 1.27163    1.54582    \"B\"\n",
       " 1.71081    1.5244     \"B\"\n",
       " 1.37372    1.70942    \"B\"\n",
       " 0.953916   1.40578    \"B\"\n",
       " 1.79862    1.52633    \"B\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = (x1 = x1, x2 = x2)\n",
    "y = vcat([\"A\" for i in 1:n], [\"B\"  for i in 1:n]);\n",
    "classes = CategoricalArray(y)\n",
    "display(hcat(x1, x2, classes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "e65ca2a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mTrain indices\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1×42 adjoint(::Vector{Int64}) with eltype Int64:\n",
       " 60  8  5  53  59  30  14  55  56  42  …  4  51  11  40  26  39  50  37  33"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mTest indices\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1×18 adjoint(::Vector{Int64}) with eltype Int64:\n",
       " 24  58  23  15  12  6  32  46  9  45  49  29  34  35  47  20  52  38"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_inds, test_inds = partition(1:nrows(X), 0.7, shuffle=true);\n",
    "@info \"Train indices\"\n",
    "display(train_inds')\n",
    "@info \"Test indices\"\n",
    "display(test_inds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "bab8cacd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNNClassifier(\n",
       "  K = 5, \n",
       "  algorithm = :kdtree, \n",
       "  metric = Euclidean(0.0), \n",
       "  leafsize = 10, \n",
       "  reorder = true, \n",
       "  weights = Uniform())"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knnc = KNNClassifier() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "761bea63",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mTraining machine(KNNClassifier(K = 5, …), …).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "trained Machine; caches model-specific representations of data\n",
       "  model: KNNClassifier(K = 5, …)\n",
       "  args: \n",
       "    1:\tSource @627 ⏎ Table{AbstractVector{Continuous}}\n",
       "    2:\tSource @555 ⏎ AbstractVector{Multiclass{2}}\n"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting the model\n",
    "knnc_mach = machine(knnc, X, classes) \n",
    "fit!(knnc_mach, rows=train_inds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "7c12e16b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mReal and predicted classes\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2-element Vector{CategoricalVector{String, UInt32, String, CategoricalValue{String, UInt32}, Union{}}}:\n",
       " [\"A\", \"B\", \"A\", \"A\", \"A\", \"A\", \"B\", \"B\", \"A\", \"B\", \"B\", \"A\", \"B\", \"B\", \"B\", \"A\", \"B\", \"B\"]\n",
       " [\"B\", \"B\", \"A\", \"A\", \"A\", \"A\", \"B\", \"B\", \"A\", \"B\", \"B\", \"A\", \"B\", \"B\", \"B\", \"A\", \"B\", \"B\"]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "expected = classes[test_inds]\n",
    "p = predict_mode(knnc_mach, rows=test_inds)\n",
    "@info \"Real and predicted classes\"\n",
    "display([expected, p])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e7074a8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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