cap13/CH13EJ7-Creacion-de-un-arbol-de-decision-CART.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; clear: both;\">\n",
    "<div style=\"float: left; width: 50%;\">\n",
    "<img src=\"http://www.uoc.edu/portal/_resources/common/imatges/marca_UOC/UOC_Masterbrand.jpg\" align=\"left\">\n",
    "</div>\n",
    "<div style=\"float: right; width: 50%;\">\n",
    "<p style=\"margin: 0; padding-top: 22px; text-align:right;\"><a href=\"http://www.editorialuoc.cat/mineria-de-datos\" target=\"_blank\">Minería de datos: Modelos y algoritmos</a></p>\n",
    "<p style=\"margin: 0; text-align:right;\">Estudios de Informática, Multimedia y Telecomunicación</p>\n",
    "<p style=\"margin: 0; text-align:right; padding-button: 100px;\">Universitat Oberta de Catalunya (<b>UOC</b>)</p>\n",
    "</div>\n",
    "</div>\n",
    "<div style=\"width:100%;\">&nbsp;</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ejemplo de reducción de la dimensionalidad y clasificación mediante árboles de decisión"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Carga del conjunto de datos\n",
    "\n",
    "En primer lugar, cargaremos el conjunto de datos IRIS dataset (Más información en https://archive.ics.uci.edu/ml/datasets/iris). Éste se puede descargar de Internet o puede ser cargado directamente de la librería `scikit-learn`, que incorpora un conjunto de datasets muy conocidos y empleados para minería de datos y *machine learning*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "### Loading the Dataset\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "target_names = iris.target_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Análisis exploratorio de los datos\n",
    "\n",
    "Utilizando una librería gráfica, como por ejemplo `matplotlib` realizaremos un gráfico del histograma de valores para cada uno de los atributos, separados por los valores de clase. La finalidad es observar como se distribuye cada uno de los atributos en función del valor de clase que toman, para poder identificar de forma visual y rápida si algunos atributos permiten diferenciar de forma clara las distintas clases que forman este conjunto de datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "### Exploratory Visualization\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2)\n",
    "ax0, ax1, ax2, ax3 = axes.flatten()\n",
    "\n",
    "# sepal_len\n",
    "for i, key in zip([0,1,2], target_names):\n",
    "    ax0.hist(X[y==i, 0], alpha=0.75, label=key)\n",
    "ax0.set_title('sepal_len')\n",
    "ax0.legend(loc='upper right')\n",
    "\n",
    "# sepal_wid\n",
    "arr = []\n",
    "for i in [0,1,2]:\n",
    "    arr.append(X[y==i, 1])\n",
    "ax1.hist(arr, alpha=0.7, label=target_names)\n",
    "ax1.set_title('sepal_wid')\n",
    "ax1.legend(loc='upper right')\n",
    "\n",
    "# petal_len\n",
    "for i,key in zip([0,1,2], target_names):\n",
    "    ax2.hist(X[y==i, 2], alpha=0.75, label=key)\n",
    "\n",
    "ax2.set_title('petal_len')\n",
    "ax2.legend(loc='upper right')\n",
    "\n",
    "# petal_wid\n",
    "arr = []\n",
    "for i in [0,1,2]:\n",
    "    arr.append(X[y==i, 3])\n",
    "ax3.hist(arr, alpha=0.7, label=target_names)\n",
    "ax3.set_title('petal_wid')\n",
    "ax3.legend(loc='upper right')\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Reducción de dimensionalidad\n",
    "\n",
    "En esta sección aplicaremos dos métodos de reducción de dimensionalidad al conjunto original de datos. El objetivo es reducir el conjunto de atributos a un nuevo conjunto con 2 dimensiones (en lugar de las 4 que tiene el conjunto original).\n",
    "\n",
    "Podemos aplicar, entre otros:\n",
    "- Principal Component Analysis (PCA)\n",
    "- Linear Discriminant Analysis (LDA)\n",
    "\n",
    "No es necesario programar los algoritmos. En este caso, haremos uso de las implementaciones disponibles en la librería `scikit-learn`.\n",
    "\n",
    "Generaremos un gráfico (en 2D) con el resultado de cada una de los métodos de reducción de la dimensión, con el objetivo de visualizar si es posible separar eficientemente las clases objetivo empleando estos métodos de reducción de dimensionalidad."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "explained variance ratio (first two components): [0.92461872 0.05306648]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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33w44CdhOP/10du7cOeLc559/nl/84hcArFixgiNHjtDT0wPAddddx6RJk7L6fSc8xXTLlHG6BhvhGyVHZ3iIKpER26pE6AwP5dTuJz/5SZ544gkee+wxVq1ahapyzz33sHXrVrZu3cqbb77JF77wBQBqa0/4j6dOncq2bdu4/PLLeeihh7j55ptHtKuqyCh749vHI9kx8bYSbTAKRJkXQTfBN0qO2b4qhkYJ4ZAqs31VObW7atUq1q9fzxNPPMEnP/lJrrnmGh555BGOHTsGwL59+zh48OBJ5x0+fJhoNMonPvEJvv3tb/PSSy+N2L9w4UL279/Ppk2bAOjr6yMcDvP+97+fn/70pwDs3LmTPXv2cNZZZ404N/GYZ599lunTpw8/IRhFoMyLoJtLxyg5VtXPYO3RfRCNUiXCkCohVVbVz8ip3XPPPZe+vj7mzp3LnDlzmDNnDjt27ODSSy8FYPLkyfzLv/wLXq93xHn79u3jpptuIhqNAnD//feP2B8IBHjssce4/fbbOX78OJMmTeLpp5/mtttu49Zbb2XRokX4fD4effRRqqpG3rTuu+8+brrpJhYvXkxNTY3lzy82PW3OyD6RMlqpa+mRjYKQaSrfjQO9rO89RGd4iNm+KlbVz8jaf1+uWHrkPPD4ipNDQkP9zsTxDRuKZ1eMoqdHFpHTgH8GZgNR4EequjbXdo3KZllNvQl8pVLMSdOlzY7PHpyRfXigrEJC3fDhh4H/qapnA5cAfy4i57jQrmEY5UA6idASjy3mpGmZh4TmPMJX1Q6gI/a+T0R2AHOB7bm2bRjGBCdVkZJUIpo4aQonXje1FE50y3ilrqtROiLSCCwBNibZd4uIbBaRzYcOHXLzsoZhlCqZRr1kkkfHyBjXBF9EJgO/AL6iqictiVTVH6nqxap68YwZuUVbGIYxQchUwJPluLf0xq7hiuCLiB9H7H+qqr90o03DMMqATAXc8ujklZwFX5xlgf8E7FDVv8ndpMLQ2rqLFSvW0dS0lhUr1tHauqvYJhkFZv/+/Xzyk5/M+LwPfehDdHd3j3lMtqmTy45MBbzMJ02LTc5x+CLyXuC/gVdwwjIB/peqPpnqHDfj8Ftbd9HS8gJtbd00NU2huXk5K1eeOe45a9a0Egh4qKnxMzAQIhiM8uCDK8c918iOiRQzPlHSEU+YPh0Os2x3Rvb5CLMs4/w3ieQah5/zCF9Vn1dVUdXFqnpB7Cel2LtJXLg7OvqYNq2ajo4+1qxpHXe03tLyAoGAh9raACJCbW2AQMBDS8sLhTDbSIdMQvnSIFV65PPOOw+ARx99lOuvv56PfOQjXH311QwMDHDDDTewePFibrzxRpYtW0Z8kNLY2Mjhw4eH0yh/8Ytf5Nxzz+Xqq6/m+PHjwPipk9vb23nf+97HhRdeyIUXXsgLL5Tx/73RRUryIfZlnP/GTSZ0Lp1shbutrZuaGv+IbTU1ftrbu/NorZE2efgCJ0uPHM9uGefFF19k3bp1bNiwgR/84AfDGTW/8Y1vsGXLlqTt7tq1iz//8z/ntddeY8qUKcOZL+OkSp08c+ZMfve73/HSSy/x2GOP8eUvfznr363i2dTiuIn69sHhV53XSLBs8t+4Sek/t45BW1s306aNrFCfjnA3NU2ho6OP2trA8LaBgRCNjVPyYKWRMXmIxU6WHnn+/PkjjrnqqquYNs3Jo/L8889zxx13AHDeeeexePHipO02NTVxwQUXAHDRRRfR3t4+Yn+q1Mn9/f2sWbOGrVu34vV6T0qbbGTAke0weNQpYO7xQTQEAwecV2MEE3qE39Q0hYGBkX/UdIS7uXk5wWCU/v4gqkp/f5BgMEpz8/I8WmukTZ5isUenRx5NYjridOe2EpOheb1ewuHwiP2pUic/8MADzJo1i23btrF582aCwWC6v4YxmsgQICAxOROP8zmSWzrtcmRCC362wr1y5Zk8+OBK5sypo6trkDlz6mzCtpTIUyz26PTIY/He976Xxx9/HIDt27fzyiuvZHXNVKmTe3p6mDNnDh6Ph5/85CdEIpGs2i8bcpmz8cSe1DXiRAJprC89uaXTLkcmtEvHEW7Hl9/e3k1jY3pROvFzTeBLlDwlsBqdHnm0+yWR2267jdWrV7N48WKWLFnC4sWLaWhoyPiaY6VO/sQnPsG//uu/csUVV1R2sZNM0i8ki8aZfi507YKhHmdU762CqgaYat/v0Vh6ZKMgZBxCWIhQvjGIRCKEQiGqq6vZvXs3V155JTt37iQQCIx/coGYMGGZ45FuSuLEG0PiQODc1fDaupO3l2H8ftHTIxtGXihyAquBgQGuuOIKQqEQqsoPf/jDkhL7siLdoiOpJvPfedYR900tzgRuZMhx88SjdMpM9HNhwgh+NgusDCNb6urqsKfQAtHQdPIIP9mczVg3hrioP7MGqqc628fLzFmBTIhJ22wXWBmlRTHch+VKWfVluukXxpvML/N6tG4wIQTfVsZOfKqrqzly5Eh5CVWRUFWOHDlCdXX1+AdPBNLNnzPejcFSK4/LhHDpZLvAyigd5s2bx969e7FaCO5QXV3NvHnzim2Ge6QzZxO/MaSazE/XNVTBTAjBL7WVsTafkDl+v5+mpqZim2EUEzcSnI11Y4iH8waPwVC3M3krXlj4qZxNLxcmhEunlFbG2nyCYWRBIRKcNa10QjSPH4bwoLPwatJ0J2TTEqkBE0TwS2llrM0nGEYWFGpC9Z1nHRfOzPPhlLOgdpZN3CYwIVw6UDorY20+wTCyIN1Y+4lynQnKhBjhlxLZJmwzjLIjk/w3hapVazVxx8QEP0NKaT7BMIpGpj75QtWqtZq4Y2KCnyGlNJ9gGEUjU598OrH2blQ5s5q4Y2LJ0wzDyJyHm5yRfWKuf1VHZL/4VubtpUqMZmI9gqLXtDVyp7V1FytWrKOpaS0rVqyzEE+j9HHbV25pEQpC2Qp+vkXUrfYtrt+YkLjtK7e0CAWhLAU/3yLqZvsW12+Mx8aBXu7s3M2n9m7nzs7dbBzoLbZJ7vvKLbqmIJSl4OdbRN1sv62tm5oa/4htFtdvxNk40Mvao/s4GglR5/FyNBJi7dF9pSP6N2xwfPY3bMjN127RNQWhLAXfLRFN5bZxU6Qtrt8Yi/W9h/CLUO3xILFXvwjreydgErqxonAsuqYglKXguyGiY7lt3BRpi+s3xqIzPERVYiQMUCVCZ3ioSBZlSTpx+24+MRhJKUvBd0NEx3LbuCnSFtdvjMVsXxVDo0Knh1SZ7asqkkVZYlE4JcGEyaWTCY6IOqLd3t5NY2PmKYzHypmTTftjpVQulTxBRumxqn4Ga4/ug2iUKhGGVAmpsqp+huvX2jjQy/reQ3SGh5jtq2JV/QyW1dS707jluCkJbOFVClasWHdSDv7+/iBz5tSxYcPqjNqKu4cCAQ81NX4GBkIEg1EbyRtpkVchTrjG2qP78IuMuLHcMW2uO9d6fMXJxUlC/Y6v/oYNubdfIZTEwisReUREDorIq260Vwq46bax0EsjF5bV1PPA7Hfx83nn8MDsd7ku9lCAyWGLwikJ3PLhPwp80KW2SoKVK89k9erz6ezsZ9u2A3R29rN69flZjcgt9NIodfI+OWxROCWBKz58VX1ORBrdaKtUaG3dxbp125g9u5YzznCictat28bSpadmLPqlVqLRMEYz21fF0UiI6gTRd31yOJ26tUZeKViUjojcIiKbRWTzRChk7aYbxkIvjVJnVf0MQqoMRqNo7DVfk8NG8SiY4Kvqj1T1YlW9eMaM0v9P5KYbxkIvjVJnWU09d0ybyzSvn75ohGlev3sTtkbJUJZhmeMxVohkHLfdMBZ6aZQ6y2rqy0Pg21qd+P6eNidHz9JmcyXFKMuFV2ORbuIzc8MYRgFxo/hJvJ3//Dzs3wh9+5zX//x89u2VGW6FZf4ceBE4S0T2isgX3Gg3H6Trmzc3jGHkSLoinmm5xLH476/B4GEgCl6/8zp42NluuBal8yk32ikEY62gHY25YYxSphALsrImsYJVoognC8VMTLsAJ143tWTuijm6E/CCxMay4gH1xrYbFefSSTfxmVWhMkqZkk6bDJnlznG7+ImM87mCqTjBT8c3b1WojFKn5NMmZyLibhY/mboANAoacVb0asT5PHVB5m2VIRUn+On45i0VglHqlHza5ExE3M20C+//bqy4utcRe/E6n9//3ax+jXKj4gQfHNHfsGE1b711Bxs2rD7JTz86Br+nZ4i9e3t57rm3zb1jlAQlnzY5ExF3M+1C00r44I9hzjKYPNd5/eCPLSwzhmXL5ERc/muvHRx29/j9Hk49tQ4Q9uzpAZRAwMe8eXWW6dIoOnnPbukGw/Hw7c7IPjEe3mLlsyLXbJkVL/hxf30wGOLgweOAEok4feLxCB6PM0ABYf78ehoaqrNOk2wYbpL3KJ18iXJiBI+vxnH1RIKWTC0NTPBzJJ73ft++PoLBCF6vh2g0ioggAgMDYSZPDjB7di0gHDhwjMHBMF6v8Mtf3mijfKM8yaco5zs3fhk/PZREPvyJTNxfPzQUweNxJsE8HiESURYunE51tZd58064dkKhSGzkLyMidyyM0ygr8lmS0O0wzETcXMRVhlS84Mfj8quqvESjztNONKpUVXkZGAixYMF0gsEo+/f3IqJOpJfCqafWDUfuWBinUXbkU5TdDMMcjdXOHZOKF/x4XH5DQwBVCIcjRCKO4Le399DZ2cfkyX5CoSiqEAh4h3358RW6FsZplB2JojzU46xUPfQyDHXlPlrOZ/WrfN6oyoCKF/x4XP6ZZ05n6tQqamoC1NYGOHYsxPTp1cybVz/s2585czILFpxCQ4OTmiG+QtcqWhlusnGglzs7d/Opvdu5s3N3cVbPxkW5/wD0vg2RISdNgX9y7i6SfFa/yufTQxlQ8ZO2yUhWwPzAgT4OHx6ksbHhpELkLS0vpFXwPJ20zEZlU1Lhlm2t8B+fdkbg3mqomQXVDcUtPj7ehGyZRwDZpG0eSDZinzlzMg0NgaQrdN1M12CTv5VNSaVMaFoJVVNg+iKYtsAReyieiySdCVmrnTsmFVkABcYebacqfnLOOTOTxt47biEnJUN7ezeNjSeP3hP9/EDsNUhLywvDx8VvCoGAZ8RN4cEHsSeBCqEzPESdxztiW1FTJjQ0nRxCWSwXSbpZNa12bkoqcoQ/3mg7m+InmaZrgJP9/Db5a5RcyoR8TrBmik3I5kxFCv54wppL8ZNULpl00jLb5K9RcsXES8lFYhOyOVORLp10iqBkU/xkLJdMc/Ny1qxpBYIjJn0TnxrcrqNrTDyW1dRzB/APXft5O+S4ceb5i5wQLV0XSb5XuC5tdnz2MHJCthhPGxOUihzh56sIylhPDuk8NVgdXSPOoCqzfQFO91cRVi2t4ibJKMQK12RPG+eudm4yudbCrRAqMiwzcSQ+OsQy2QRqqmNG09S0lmnTqpGEPOWqSlfXIG+9dUfato01+WuUP3d27uZoJES158R4bDAaZZrXzwOz31VEy8Yg3/lxklHmIZjJyDUssyJdOsmiai6/vJGWlhe47bYnaWqawpEjA+NG1YzGDZeM1dE1Si5SJx162pyRfSL5nlB1sxZuhVCRLh0YGVXT3Lycdeu2jYja2b79MKFQBICenkF27jzCW2918eKLe1O6dswlY7hByUXqpEMxJlQtaidjKlbwE0nle+/oOEZPzyB79vQSDEYQAY+HlInRconuMYw4JRep09bquGzG8pOnCt887fLxz80Wi9rJmIr04Y8mme+9u3uQt9/uIRDwEIkoIhCNwvz5Dfh8YgVQjLyS9+Im6V4nEz/56ApXp10Or63Ln4/dfPiZn2+Cnzx3Tn9/kEDAx86dh4lElOpqH7NmTaahoSrjiVjDKEXSytuTy2RsISZyxyqjWIbYpK0LpIqR/5u/uTJpYjSLjTfKgcS8PQDVscfY9b2HTgh+LpOxhZjItTQKGWGCz9i5cDZt2s/99z9PJBKlqsrHlCkBAgG/TcRWONm4XHJx0+TDxZNWNFAuuXRKKQ+PAdik7TDJcuG0tu5i3bptTJ9eQ1WVl8HBMIcPD7J69fk2EVvBxF0hRyMh6jxejkZC4y6MyuYcN84di7SigXLJpVNKeXgMwHz4Y5LKt28TtpVNNgujkp3TFQlxPKpM9njGHLVncr3EJ4FJ4qRYHohGkrafdu79XPzkFeZjzzcl4cMXkQ8CawEv8I+q+l032i02qXLubN9+kPPP/3t27jwMCAsWTOO73/2AjforhGwWRo0+pz8aoSsSRoE5Hv/wqP0OOEn0071eooCLwp6Is3+WN3n78bw947qKcvGTm4+9pMhZ8EXECzwEXAXsBTaJyL+p6vZc2y42yVbOHjx4jK6uIY4cGcQb+w7u2HGYm276NT/+8UdN9CuA2b4qZ8SdEMY73sKo0eccjYQBCMRG4UknTDO8XuIk7KFICA8gCF3RCKf5fUnbX1ZTn/9KWolJ1QL1IMBQr+Pjn3wqvPUfEOyDQB1cdCdcem9+7alg3PDhvwd4U1XfUtUgsB74qAvtFp1kK2cPHx5EBHw+wev14vV68HiEvr4hy1tfIWSzMGr0OUGNAjA1YeSe6ikh3et1hoeoit0UQqoIIOK8H6v9vJKYVE28cHQHHNnuvD/4Muz4qSP2ngCEBuDFb8OL3yqsjRWEG4I/F3gn4fPe2LYRiMgtIrJZRDYfOlSEcm1ZkGzlbENDFdGo4vGcGG15PEI4HLW89RXCspp67pg2l2leP33RCNO8/nFrzo4+Z5J4meLxMdl74iE71VNCsutdUzuV9b2HRhQ6T5yE9YugOHOl/thNoCjpGRLz3QwcdAqhi895H+yOHaTOncnjc/ZvecDZnM4KXyMjcp60FZHrgWtU9ebY588A71HV21OdM1EmbZOxYsU6Nm7cGxN9534ZiUTxeoVly+bR3LzcCpUb45JLsfJU515TO5Wn+rvwixCKRjkYdVKAz/L68YnH3WLo6ea+f7jJicUXgUOvOCN7EYiGIRqMHSTgm+S8VYVoCD7+bxW3ijYdSqGI+V7gtITP84D9LrRbkjQ3LycQ8DI0FGFgIMTx4yFCoQh1dVVcfnljWoXKDWNZTT3X1E7laCTM7tAgRyNhrqmdmpYYpyp0vnWof/hJQAXm+6o43V9NFNJ6CkmbTHLfJ+a78VYBUdCo815i7qyEuQk0AoHJI58MRJxXb8DZbmSNG1E6m4AzRaQJ2AesAj7tQrslwehi55df3kh1tY/jx8MMDUWcR2a/h9tuW8qzz7ZnnFLZqDw2DvTyo+4O3g4N4hNhlseP3+Phqf4uFlbVjCvKo6N2jkXCdEUj7A0PQS95y7szTCZpiROrVNXMhL49gELNXBjshqEjgDgje404N4OL7oRXf5zfVbr5rs5VouQ8wlfVMLAGeArYATyuqq/l2m4pkKzY+f33P4/P5+W882Zy0UVzePe7p+Hzebj//ud58cW9wymV41hNWiORuDtmX2gIDxBV5VA0RFij+EVY3zv+/Fair/5YJMzBSIiQRgkgri3KGpNM0hInVqnSKEw7G045x3lfPxcmzXTEPnIcvH649BtOlE4+M2EWojpXieJKHL6qPgk86UZbpURi2mRwRuyRSJTu7uPMmlVLT88Qe/b0IKKogs/nYc+eXkSEhgYnft/y7hiJxN0xEcAbc2VEVemKRpjn86YVRbOqfgZrj+6DaJSuqDPAEIRTfH5ncVaK8E7XyDRlQrJY/LjoTpoGdfNO+OhnL3X257N+bQUXTrHUCmPQ1tZNTY1/xLaqKh+Dg86X7MCBYzjztkJVlY9TT61HFfbv77MCKEZS4qGTfhHi8RKCEzqZbhRNYtTOkEbxIcz0+amNuXnyHn7pRsqE8Xz0yerXujVhW8GFUyx52hgkW3g1ZUqAw4ed2PzBQWfxTDgcJRyOcuDAMerq/PT0DPHyyweYPLmKO++8xPz3xjDxRVTTvD4OhkOgoIA3Fi+fbpGT+IKpZGkX8h5+GRfjXFImpJNJM1+rdCs4qZuN8Mcg2cKrQMDPPfe8lzlz6gBH7H0+iSVXC9HVNYTf72Xx4lnMnl3LunXbLErHGCa+iMqLMMPrQ4AocKo3kFUUTdGqYzWtdHLaf/Et5zVTYS5mtaoKTupmgj8GqUoW3nvvZWzYsJqzz56Oz+fB4/GgCuGw84zu8ciIUom2AteIk+iOiQJnV9XylzOaeGTuwrTEfuNAL3d27h5ecAVkvAisJEgU3cFuOLwDut6E40fyP3maT3dRiWPZMnOgqWktXi8cPDjA0FCEUCiC3++URKyp8TM0FCEQ8FJb66Ozs/xHD4a7jM6Bf0FV7fDCqkwXa5UEo0MhT7scdv0CDm93/Pe1c5xIHVtglZJSWHhVsTQ1TcHv97JgwSksWjSTyZOdKJ5oVAkGI3i9QjAYpqcnmNSt09q6ixUr1tHUtJYVK9aZ68cYJlkO/J/1HiRM9KQFV+mEchadZKGQr61zJjCmvhtOORuqp9gCqzxjgp8Do338DQ0BolHHpeP1Cs7TkzB9es1Jbp1kMf62KteIk2w1bQSlLzJynUdREqJlQ6qonK6dFRsxUwxM8HNgtI//zDOn09BQzaRJPsJhxe/3Mn9+AzNn1py0+Coxxt/8/cZoEjNfxgmIhxDjVKgqVVKFQkLxJm8rEAvLzJGVK88cEXaZqkrW6MVXqYqr2KrcymS0v36SeBhSHZEDv1Y8RGKROIk+/Auqarmzc7er9W5dJ1Uo5LQFEDzmfHZ7gZVxEjbCd5lkoZyJi6/ifvvOzj5ef/0wPT2Dw+faqtzyY3RUTbKUB8n89T3RCH2R8IhwS794+HT9zJPSJD/V3+V6vdusGCudcapQyPd9t2IjZoqBRenkgXjCtfb2bhobT6RIjvvtAwEPoVCUt9/uQQTmz6/H7/cSDEZ58MGVtlCrTEg3BXKqmrU+Eeo9vjFH7tnU180L8UnZsdIZW33bnMk1SscEv0C0tu7i05/+JceOBZk0ycesWZMB2L+/l2gULr10nuXOLzNGi/GxSJhDkTBhFD/CVI+XWq+P9tAgVeJhqsc7XBBFVemLRvj5vHPGvMan9m6nzuNFElw/6Z7rKo+vONllE+p3Ruw3bCicHWVOSRQxN8YmPrLv7w/i9wuhUIQ9e3qYP7+BhQun09U1yIYNq4ttpuEyiWmMj0XCHIiEiMfYhFEORMN4omG8QEijHIw4ZQ8ne31pT8ZmU183L6STKsEoOubDLwDxiJzqal8sbNODx+MkXzO/ffmSmMa4KxoZFnuB4VgbxUmtEAHCQGckxNFwKO30CEVLrTCaYqZKMNLGBL8AxLNuzppV66T+jkQRgePHw5ZNs4xJFONQrGg5gA8ZIfgRTnwRo0BvNJJ29at4qgYv8HZoiM5wkEmeInytKzg/zUTCBD+PjI7I6e8P4fFAMBhhcDBCVZXXJmnLmMS8OR4EwfGhesQZ5SfiEyGAMEk8zPYF2DrUn9G1BlWZ7Qtwur+KsGrhI3UqOD/NRMJ8+HkiMSJn7tx62tu76eg4hs8HgYCXaFSZPDkwfkPGhCaexnjjQC/fO/IOPdEwUdURbh0fsQp/wDSvL+PVs4mrcgHHn5/vIijJyFc6Y8M1bISfJ0aupGW42EU47KwsP/30BqZOrbaVtRXCspp6/uKU0zjdX40g+BBmeXxUiyeWD/9EEZNMJ12TrcqdMCkXMmGsOH8jLWyEnyfiK2l7egbZs6eXaPRE+Gs05s61lbWVRXy0n8joWP1sJl1LJlInnyTG+SfWoZ1IbqMSKJxuI/w80dQ0hYGBEAcO9CMSz5HvjO5DoQi7d3fx+uuHqaszt04lk+jnzzaffclE6uST8UoiljolUjjdRvg5EF9R29bWTVPTlBELpy6/vJH773+ewcEwIuD1CuHwyPODwSgHD/bT2rrLJm6LxOgcNsXIQ5Ns5J/p+XdA0X+PvDLR4/xLpHC6CX6WJE7KJqY3fvBBZ/+6dduYPr2ajo5+IhElElF8Pg/RaJRo1LkBNDZOwecTWlpeMMEvAonulMQ8NHdA0cQy2xtQrjeNkmei16EtkRuWCX6WJE7KArHX4PAkbCDgYerUOqqr/ezZ0wsowWCUqiovqk7+nIaGKlTV/PhFomSiW2KU4g0oLQrhm17a7LhAYGJm1SyRG5b58LMkvpgqkfgkbOK+hoZq5s+vJxBw7q1er8TE3kmNbCtti0epRbckK3pS6hWtXn/9Fxz+7ZfY2/0273hrGejblx/f9ESP8y+RhWk2ws+SpqYpJ+W9TxTvxH0NDdX4fB4CAS/HjoXw+TyoKgMDIVtpW0TyGd2SjWsmMfdOnFA0yvZwP5/au73kfPMbB3qZvOl7DImfsH8SqtApfmYDNfnwTU/kOP/4DavI2UJthJ8lY+W9T7Xvu9/9wIgKWXPm1NlK2yKSr+iWZPnt01n5mph7B5yEawejIQSStpNOrv2sSDPefX3vIWb27yPsq0Y4sYL4iPgnzmRqIWla6WQO/eJbzmsRbl4m+FkyurxhIOBj8mQ/t932JC0tL7B69fknCTswHNWTmCffKA5uhEQmI1vXzOgb0JGoE9Y13ec/qZ34yt0dQ/0cioTYMdTP9468k7voZxA+2Bke4kjtPAKRE0V8REDCx/Prm7YFWFmTUz58EbkeuA84G3iPqqaV5L7c8uEnRuzU1PiHXTWJo/d0jjHKg1xy1Ce6go5Gwkz3+oZz5Ce2U+Px8nZoEC8yvJI7gnK6v5p/OvWs7I3PIK/9nZ27mfvOM6ze8n8T9voJeqvxhQep0hDTr/6H/Ixg0ym0kk2bRV4QlS655sPPdYT/KvAnwHM5tjOhSacgebJjgsEIn/70L2lqWsuKFetobd1VxN/CcIvRrhlIf25gWU09D8x+Fz+fdw7nVNXik5Ff0Xg7e0NDeHBG1MRePcDeUI4TzqmKjSdx0ayqn8FLc97Huov+Nz3VM5gU7KF70nQOX/ZA/gTT7QVYJbIgqlDkJPiqukNV33DLmInKWBE7qY7p6RniwIFj9PcHR8Txm+hPfNyaGyjKCtoM8trHXWL7TruS/3PFI9z/sf+PY5/4HQsXfiJ/9mVwQ0qLib6CN0MsSscF4hE74XCUAwf6GRqK4PMJ7373tJOOiUfuHDhwDBGoqvINj/jjcfzm4pnYuLXydax25voC7AkPQULmzSgw35djqo4M490LvuDL7Xj2ElkQVSjGHeGLyNMi8mqSn49mciERuUVENovI5kOHSjeuOBuam5fT1TXI22/3EAxGENERaRPixyRG7hw/HgaEWbNO/Me1ZGrlQ6Jr5oHZ78paFFO186Wpp1IvXjwiRAGPCPXi5UtTT83N8FKPd3c7nr3CKnW5UsRcRJ4F7qrUSVuA88//e9588wjhsFJV5WXWrMn4fMKcOXXD9WrjuXfa27vp6hpk8mQfs2bVDbfR3x8ccbxhjEUp5AEqCsOTrO25x7PnYxI4j1gR8xKht3eIhQunnxSZkThiX7nyzJOidvr7gyOidmwRlhFnPEEv+/w5qXBzAVaJLIgqFDlN2orIx0VkL3Ap8B8i8pQ7Zk084umQExkrbcLoOH5bhGUkku3iLSMLSmBBVKHIaYSvqr8CfuWSLROa5ublrFnTCqQ/Yk8c8RuVQbpumFJL7GaUB7bS1iVsxG6MRyaj9lJL7GaUB+bDdxEbsRtjkcmovSLKFhoFx0b4hlEgMhm1V0TZQqPg2AjfMGLkO8wxk1F7RZQtNAqOCb5hUJhqU6vqZ7D26D6IRqkSYUh1zFF7xYZdGnnDXDqGQWGqTeUrHbNhpIuN8A2D5NWm8hEVMxFG7SW/gncCpTMuNWyEbxjkltK4kOStylVC+yW94KvC0hm7jQm+YTAxomIKIcYlX0i9wtIZu40JvmEwMfzrhRDjkl/w5XY+/ArDfPiGEaPU/euFmGco+QVfbufDrzBshG8YE4RCzDOUvGvL7Xz4FYYJvmFMEAohxiXv2ir1Ai0ljrl0DCNNih2uuKymnmuGBnii7zDHNIIXmCSeYR++W7aUumvL1Xz4FYaN8A0jDUohXHHjQC9P9XdRLYIPEGBQo+wLD5ZW6KRRstgI3zDSIJf89G49GcRt6I5GEZw6tqpwLBplhtdrufKNcTHBN4w0SDdCZrS4X1BVy1P9Xa7k6InbEFIdfjQXgZBqaYVOGiWLCb5hpEE64YrJErD9rPcgDV4vdR4/kFvlqrgNfhHCqghOoIo/logtm2idYs9LGIXFfPiGkQbpRMgkWxgVQemLREa0le1oPG5DrXhQIKJKFGWyx5NVtE4pzEsYhcUE3zDSIJ1wxWSrVAPiYQjlndAQbwUHeSc0RFcknNVoPG7DPH81k8VDtXip8/iY66vOKnSy5NMoGK5jLh3DSJPxwhWTuX18CgqENIoHx9/epVE+XFWbsp1cbMiEQmUINUoHG+EbRgaMla0ymdtnCKVBvPjFQxTBLx6meX1sHeov4m/hMFEyhBruYYJvGGkyns87mdun1uNlps/Paf4qzghUc5q/iikeX0mMoks+jYLhOubSMYw0SScWf7TL5c7O3SWbjMzq5lYeJviGkSbZ+LwzrWNbaEo+jYLhKubSMYw0ycbnXfLJyIyKwkb4E5DW1l20tLxAW1s3TU1TaG5ezsqVZxbbrLIn1Wj9gqpa7uzcndItkq9RtC2aMjLFRvgTjNbWXaxZ00pHRx/TplXT0dHHmjWttLbuKrZpZU+y0fo1tVN5qr+r4IuXbNGUkQ05Cb6ItIjI6yLysoj8SkSmuGSXkYKWlhcIBDzU1gYQEWprAwQCHlpaXii2aRXBspp6Hpj9Ln4+7xwemP0utg71F2Xxki2aMrIh1xH+74DzVHUxsBO4J3eTjLFoa+umpsY/YltNjZ/29u7iGFThFKsGbMnXnjVKkpwEX1V/q6rh2MffA/NyN8kYi6amKQwMhEZsGxgI0dg4pTgGVTjFWrxki6aMbHDTh/95oDXVThG5RUQ2i8jmQ4fssTNbmpuXEwxG6e8Poqr09wcJBqM0Ny8vtmkVSbEWL9miKSMbREeNEk46QORpYHaSXV9X1V/Hjvk6cDHwJzpeg8DFF1+smzdvzsJcA05E6bS3d9PYaFE6xaZY0TIWpVN5iMgWVb046/PT0OfxDFgN3ApcqaoD6Zxjgm8YFUZbK2xqgZ42aGiCpc1WlzYLchX8nOLwReSDwN3AZemKvWEYZUwyYQd4Zg14A1A9Dfo7nM9XPmiiX2ByXXj1IFAF/E6ciIHfq+qtOVtlGMbEo601ubAHJjvb/LGU0PHXTS0m+AUmJ8FX1Xe7ZYhhGBOcTS3Jhf3oTjjlnJHH+mqgp72g5hm20tYwDLfoaXOEPJH45/Aoj294ABoaC2KWcQITfMMw3KGhKbmwT10AkSCE+p2q66F+53Pcv28UDBN8wzDcYWlzcmF//3edCdraOTDY5bzahG1RsGyZhmG4Q9NKR8g3tTj++YbGkeGXJvBFxwTfMAz3aFppwl7CmEvHMAyjQjDBNwzDqBBM8A3DMCoEE3zDMIwKwQTfMAyjQjDBNwzDqBBM8A3DMCoEE3zDMIwKwQTfMAyjQjDBNwzDqBAstYJhFICNA738qLuDvaEhAOb6Anxp6qlWg9YoKDbCN4w8s3Ggl+8deYe3Q4No7N+e8BB/dXgPGwd6i22eUUGY4BtGnlnfe4j+aAQvgkdiP8AAUdb3Hiq2eUYFYYJvGHmmMzxEGMUp++wgQESVzvBQ0ewyKg8TfMPIM7N9VfgQVE9sU8ArwmxfVdHsMioPE3zDyDOr6mdQ6/ESQYlq7AeowcOq+hnFNs+oIEzwDSPPLKup5y9OOY3T/dVI7N98XxV3T59vUTpGQbGwTMMoAMtq6k3cjaJjI3zDMIwKwQTfMAyjQjDBNwzDqBBM8A3DMCoEE3zDMIwKQTRxNUihLipyCHg7w9OmA4fzYI5bmH25YfblhtmXGxPFvtNVNevFG0UR/GwQkc2qenGx7UiF2ZcbZl9umH25USn2mUvHMAyjQjDBNwzDqBAmkuD/qNgGjIPZlxtmX26YfblREfZNGB++YRiGkRsTaYRvGIZh5IAJvmEYRoVQMoIvIteLyGsiEhWRi0ftu0dE3hSRN0TkmhTnTxOR34nIrtjr1Dzb+5iIbI39tIvI1hTHtYvIK7HjNufTplHXvU9E9iXY+KEUx30w1q9visjXCmhfi4i8LiIvi8ivRGRKiuMK1n/j9YU4/G1s/8sicmE+7Uly/dNE5L9EZEfsu3JHkmMuF5GehL/7vQW2ccy/VzH7UETOSuiXrSLSKyJfGXVMQftPRB4RkYMi8mrCtrS0LKvvrqqWxA9wNnAW8CxwccL2c4BtQBXQBOwGvEnO/x7wtdj7rwF/VUDb/xq4N8W+dmB6EfrzPuCucY7xxvrzDCAQ6+dzCmTf1YAv9v6vUv29CtV/6fQF8CGgFadC4SXAxgL/TecAF8be1wE7k9h4OfCbQv9/S/fvVew+HPX37sRZyFS0/gPeD1wIvJqwbVwty/a7WzIjfFXdoapvJNn1UWC9qg6pahvwJvCeFMeti71fB3wsL4aOQkQEuAH4eSGu5zLvAd5U1bdUNQisx+nHvKOqv1XVcOzj74F5hbjuGKTTFx8F/lkdfg9MEZE5hTJQVTtU9aXY+z5gBzC3UNd3iaL2YQJXArtVNdMV/66iqs8BR0dtTkfLsvrulozgj8Fc4J2Ez3tJ/p98lqp2gPPFAGYWwDaA9wEHVHVXiv0K/FZEtojILQWyKc6a2GPzIykeC9Pt23zzeZxRXzIK1X/p9EWp9Bci0ggsATYm2X2piGwTkVYRObewlo379yqVPlxF6kFaMfsP0tOyrPqxoBWvRORpYHaSXV9X1V+nOi3JtoLEkqZp76cYe3T/P1R1v4jMBH4nIq/H7up5tQ/4IfBtnL76No7b6fOjm0hyrmt9m07/icjXgTDw0xTN5K3/RpubZNvovija/8URRohMBn4BfEVVe0ftfgnHTXEsNm/z/wJnFtC88f5eRe9DEQkA1wH3JNld7P5Ll6z6saCCr6ofyOK0vcBpCZ/nAfuTHHdAROaoakfsEfFgNjYmMp69IuID/gS4aIw29sdeD4rIr3AexVwRrHT7U0QeBn6TZFe6fZsVafTfauDDwJUac0wmaSNv/TeKdPoir/2VDiLixxH7n6rqL0fvT7wBqOqTIvIDEZmuqgVJDJbG36vofQisBF5S1QOjdxS7/2Kko2VZ9eNEcOn8G7BKRKpEpAnnbvuHFMetjr1fDaR6YnCTDwCvq+reZDtFpFZE6uLvcSYqX012rNuM8ot+PMV1NwFnikhTbNSzCqcfC2HfB4G7getUdSDFMYXsv3T64t+Az8YiTS4BeuKP3oUgNl/0T8AOVf2bFMfMjh2HiLwH5zt+pED2pfP3Kmofxkj5VF7M/ksgHS3L7rtbqNnoNGarP45z1xoCDgBPJez7Os6M9BvAyoTt/0gsogc4BXgG2BV7nVYAmx8Fbh217VTgydj7M3Bmz7cBr+G4MgrVnz8BXgFejv1HmDPavtjnD+FEe+wusH1v4vggt8Z+/r7Y/ZesL4Bb439jnMfoh2L7XyEhmqxAffZenMf2lxP67UOjbFwT66ttOJPhywtoX9K/V4n1YQ2OgDckbCta/+HceDqAUEz/vpBKy9z47lpqBcMwjAphIrh0DMMwDBcwwTcMw6gQTPANwzAqBBN8wzCMCsEE3zAMo0IwwTcMw6gQTPANwzAqhP8f3dilTa8KGIEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn import datasets\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "target_names = iris.target_names\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "X_r = pca.fit(X).transform(X)\n",
    "\n",
    "lda = LinearDiscriminantAnalysis(n_components=2)\n",
    "X_r2 = lda.fit(X, y).transform(X)\n",
    "\n",
    "# Percentage of variance explained for each components\n",
    "print('explained variance ratio (first two components): %s' % str(pca.explained_variance_ratio_))\n",
    "\n",
    "plt.figure()\n",
    "colors = ['navy', 'turquoise', 'darkorange']\n",
    "lw = 2\n",
    "\n",
    "for color, i, target_name in zip(colors, [0, 1, 2], target_names):\n",
    "    plt.scatter(X_r[y == i, 0], X_r[y == i, 1], color=color, alpha=.8, lw=lw, label=target_name)\n",
    "plt.legend(loc='best', shadow=False, scatterpoints=1)\n",
    "plt.title('PCA of IRIS dataset')\n",
    "\n",
    "plt.figure()\n",
    "for color, i, target_name in zip(colors, [0, 1, 2], target_names):\n",
    "    plt.scatter(X_r2[y == i, 0], X_r2[y == i, 1], alpha=.8, color=color, label=target_name)\n",
    "plt.legend(loc='best', shadow=False, scatterpoints=1)\n",
    "plt.title('LDA of IRIS dataset')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Aplicación de CART\n",
    "\n",
    "En esta última parte, aplicaremos un método supervisado, concretamente utilizaremos árboles de decisión, para evaluar la precisión obtenida sobre:\n",
    "\n",
    "- El conjunto de datos original (con 4 atributos)\n",
    "- El conjunto de datos reducido (por ejemplo, con el método PCA)\n",
    "- El conjunto de datos reducido (por ejemplo, con el método LDA)\n",
    "\n",
    "Notad que es necesario que generéis un conjunto de datos de entrenamiento para calibrar el modelo, y otro conjunto de test para evaluar la bondad de cada uno de los modelos creados.\n",
    "\n",
    "Para simplificar el ejemplo, se pide utilizar los árboles de decisión con una profundidad máxima limitada a 3 niveles.\n",
    "\n",
    "Indicad los valores obtenidos de exactitud (*accuracy*, ACC) para cada uno de los tres modelos y la matriz de confusión asociada."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original datasets - Accuracy is  95.55555555555556\n",
      "[[16  0  0]\n",
      " [ 0 10  1]\n",
      " [ 0  1 17]]\n",
      "PCA - Accuracy is  97.77777777777777\n",
      "[[16  0  0]\n",
      " [ 0 10  1]\n",
      " [ 0  0 18]]\n",
      "LDA - Accuracy is  100.0\n",
      "[[16  0  0]\n",
      " [ 0 11  0]\n",
      " [ 0  0 18]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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2y33slyUaZ8+0GlCXAYcnOTTJXsBxwIUTrql5SQJsAq6qqjMnXc+Y2TNDsl/sl2GNu2eaDKiquht4E/ANuotwn6+qKyZb1f2SfAa4BHh8kq1JTpp0Tb1nAycAz0vyvf7x4kkXNQ4t94z90h77ZbeNtWeavM1ckqQmR1CSJBlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQm/T/V0o/esXtiOAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "# dataset slicing\n",
    "X = iris.data\n",
    "Y = iris.target\n",
    "target_names = iris.target_names\n",
    "\n",
    "# Spliting dataset into train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.3, random_state = 100)\n",
    "\n",
    "clf = DecisionTreeClassifier(criterion = \"entropy\", random_state = 100, max_depth=3, min_samples_leaf=5)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# ORIGINAL DATASET\n",
    "y_pred = clf.predict(X_test)\n",
    "print(\"Original datasets - Accuracy is \", accuracy_score(y_test, y_pred)*100)\n",
    "conf_ori = confusion_matrix(y_test, y_pred)\n",
    "print(conf_ori)\n",
    "\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "X_r = pca.fit(X).transform(X)\n",
    "\n",
    "# Spliting dataset into train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_r, Y, test_size = 0.3, random_state = 100)\n",
    "\n",
    "clf = DecisionTreeClassifier(criterion = \"entropy\", random_state = 100, max_depth=3, min_samples_leaf=5)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# PCA\n",
    "y_pred = clf.predict(X_test)\n",
    "print(\"PCA - Accuracy is \", accuracy_score(y_test, y_pred)*100)\n",
    "conf_pca = confusion_matrix(y_test, y_pred)\n",
    "print(conf_pca)\n",
    "\n",
    "lda = LinearDiscriminantAnalysis(n_components=2)\n",
    "X_r2 = lda.fit(X, Y).transform(X)\n",
    "\n",
    "# Spliting dataset into train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_r2, Y, test_size = 0.3, random_state = 100)\n",
    "\n",
    "clf = DecisionTreeClassifier(criterion = \"entropy\", random_state = 100, max_depth=3, min_samples_leaf=5)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# LDA\n",
    "y_pred = clf.predict(X_test)\n",
    "print(\"LDA - Accuracy is \", accuracy_score(y_test, y_pred)*100)\n",
    "conf_lda = confusion_matrix(y_test, y_pred)\n",
    "print(conf_lda)\n",
    "\n",
    "### plot\n",
    "fig, axes = plt.subplots(nrows=1, ncols=3)\n",
    "ax0, ax1, ax2 = axes.flatten()\n",
    "\n",
    "ax0.imshow(conf_ori, cmap='binary', interpolation='None')\n",
    "ax0.set_title('Original dataset')\n",
    "\n",
    "ax1.imshow(conf_pca, cmap='binary', interpolation='None')\n",
    "ax1.set_title('PCA')\n",
    "\n",
    "ax2.imshow(conf_lda, cmap='binary', interpolation='None')\n",
    "ax2.set_title('LDA')\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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