Decision_Tree.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CB: A decision tree is a graph and associated rules for traversing that graph.\n",
    "# A path in the tree starts in a root node and ends in a leaf node.\n",
    "# Each junction from the root node represents a \"decision\", \n",
    "# or a \"sub-decision\" on the way to the desired decision (represented by a leaf node).\n",
    "# The decision tree encodes a model for how to traverse data in a way that is useful (e.g. classification).\n",
    "# For example, the number of nodes (junctions) in the tree is a modeling choice.\n",
    "# We can also automate the creation of the tree itself, using math to determine optimal tree structures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "length = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create A distribution of points.  \n",
    "point_distribution_A = np.random.randint(low=-10, high=20,size=(length,2))\n",
    "# Split for plotting.\n",
    "x_A,y_A = point_distribution_A.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create B distribution of points.\n",
    "point_distribution_B = np.random.randint(low=-30, high=0, size=(length,2))\n",
    "# Split for plotting.\n",
    "x_B,y_B = point_distribution_B.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 13,  15],\n",
       "       [ 19,  11],\n",
       "       [  7,  15],\n",
       "       [ -5,  -9],\n",
       "       [-10,  16],\n",
       "       [  7,  -1],\n",
       "       [ 12,   0],\n",
       "       [  0,  17],\n",
       "       [ -9,  17],\n",
       "       [ 13,   6]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at the data.\n",
    "point_distribution_A[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -9, -12],\n",
       "       [ -5, -18],\n",
       "       [ -1, -20],\n",
       "       [-21, -25],\n",
       "       [-23, -21],\n",
       "       [-19, -12],\n",
       "       [-21,  -2],\n",
       "       [-18, -22],\n",
       "       [ -4, -29],\n",
       "       [-19, -24]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "point_distribution_B[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 13,  19,   7,  -5, -10,   7,  12,   0,  -9,  13]),\n",
       " array([15, 11, 15, -9, 16, -1,  0, 17, 17,  6]),\n",
       " array([ -9,  -5,  -1, -21, -23, -19, -21, -18,  -4, -19]),\n",
       " array([-12, -18, -20, -25, -21, -12,  -2, -22, -29, -24]))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirm splits.\n",
    "x_A[:10], y_A[:10], x_B[:10], y_B[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x116c4b0d0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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adJ28KW96wyV2+dRzLzS35x1zwfzLW7pyN3kavmuqzuv3+NxAXi68STu+c/CDVtfDNMZPX/PPOKCHjms/oIdi5ymXWq9JiLXpQ5wT4G6P3thI18mb9OzRq+x07gWrgeGLXrw7l77W+wWr84+5wFbDd03VMQGPMYe8XHiTdvzFvSdbXQ/TGOe97+PYOncFfoN+PK+C36AfW+euaAVdLdckxNr0Ic4JCDdWUDWiAdV0GB4e1pGRETedXTEVB1VQmAABrtjvZuxUMK6ho7Wquv+CzFp6k2lWeGzlW6sdPNA1SQGv17ViRGRUVYezjqUbeDXlTUtfdmC0Tp3bBp958kXy+kPqv40pN9pUd7yOPG9jTRuHa9LEnPA8Qs7fr5J05Zqhs7LbZ57hV+e2wXetm6pjAjXEHEx1VN7zjZ8Z645Xrd3m1rRxtCasif9SQo0VVE26Tn7bxuz2px71q3Pb4DtPvuqYQA0xB1Nu9E8feSrz/LWbd1au3ebWtHG0Jk3NCc8j1FhB1aQr1+TlmNvkqvskhDz5qteq4v5tc6DHVCvPp56wpo2DNWlqTvhENHFv3TScfJZuXZPeWykp2OAZkw5rok8ER0/5s0q127yaNkc7GWFi/TllvT5l24oQv1xj0q2HzopHezfhO08+AUw67OmvPSLz/MXzBirXbneecqk5T94ReTakrNenbFtR4nfyJt1628Z4tHcTvvPkE8Ckw177j39lrDtetXZ76rkfMOfJOyLPhpT1+pRtK0r8efLMKybEipTzxVO2LY+08+SpW8dBkXx/1tIvhUmbTjlfPEbbTM9ruCJ+uYa6dfgUyff3/YxA5ORp0ynXcIktF375ui3G5zVcEb+Tp24dPkXy/X0/IxA5edp0yjVcYsuFX7s5Q4XIaS9C/HINEE/ee1Mpku8fwjMCEVMkTz6VHPqYcuHHDDFRU3sR4r+TTwVPddVroUhdfJe19FNeWwNF6uWHrFunSie7q9f2ItDJh0Dq+nORuImrWEvqa2sgT5uOTbdOmcXzBqzai0AnHwKp689F4iauYi2pr62BPG06Nt06ZVYsmmN8XsMV8efJpwBz/auDa0saQNp58nnEkmedl+sfiw1FcWhfZl54wecoTDnmrItCYiPdO/mOFtv9p/qkyWGmV5rm+hfvBn7xnThsKILDa9TJC+9OG5w8qQ/fPnUHTt3ySasxTH29c+50fH9090vaKXUQ3+TdyaerycekxZr0520b47GhCA6vkSkv/KO/HLLW9k19rd28k3VRSHSkK9fElmedlet/w5Lsc0O1wRaH1yg3L9zyOQpTX6bc5VTyy0mapOvkU6hpE6MNNhq7Q/uOnToZc/9wK/71ZdfjWHkST+g0fO658zD6528q1FdW/ZM+kUxHXyS/PFRtP9R52eDShhTWI125JoWaNrHZYJuT7tC+L8zehs9O+iZmHPIkDhFgxiFP4rOTvokvzN5m3Zcpj/y04w7PPN+25kuoNc9DnZcNLm1IYT2AlJ18CjVtYrPBVmN3aN+pj3w5c9/UUx/5snVfpjzyX/8uW5axrfkSas3zUOdlg0sbUlgPIGW5Bkijpk1MNhTR2F3Z5zgGk1X/5GPfvS/zXFtNPtT9V0Odlw0ubUhhPYCSd/Ii8i4ReUBEnheR4YOOXSYi20XkYRE5u9w0G0xMdVdc1psJcOxCNV8yrt+xUyfj3EPuwl2HXoxHD3s37jr0Ypx7yF3ea8ekUNPGpQ15fa27dzdOX3k7Zi29CaevvD1oCaesXLMVwDsA3NndKCKzAZwP4PUAzgHwVRHpe+nHSS6x1V3xGUOoYWzrmi+G6/fto77rLH7gkhRq2ri0wdTXmSf0R6XVl3LyqvqgqmYJVAsBXKeqf1LVxwBsB/CGMmM1kphy/QG/MYQaxrau+WK4fq99/L+cxQ9ckkJNG5c2mPq646F9UWn1Tp54FZGfAPgXVR1pv/8KgE2qek37/ZUAblbV72V8dgmAJQAwODg4d8eOHaXnkwysuxI3xutngtc1BkLcR7bUE68i8mMR2ZrxszDvYxltmd92VV2jqsOqOtzfH+H2Y1Vq5j417hgJLX5huk4m5ZLXNQpii11M6ORV9Y2qemLGz405H9sFoLsg8gwAT5SdbHBUrZnHlifvkxDjF6brN/dCXteIiS12UVWe/HoA54vIYSIyC8AQgJ9XNJY/qtbMY8uT90mI8QvT9Vuwmtc1YmKLXZTS5EXk7QC+DKAfwH4A96nq2e1jywC8D8BzAD6qqjdP1F909eSpmYcDrwVpMJVVoVTVH6jqDFU9TFWP6jj49rFPq+prVfX4Xhx8lPjeh9T0mdC06TpwHb/wuYZNvH6kMtIta1AHPvchNX1mwyXhadN1MHSWXXsePvX9EGMLJGro5Mvgcx9S02dGrwpPm66DbRvt2vPwqe+HGFsgUZN27Zo6cFF7pUjdFdMxHctuT6UGvQmXtWt87kUQ2z4IJHh4J9+NLy20iJ7sMgc7VA3YZl4uNfnEa/CQZkEn38GnFlpE2zdpzTPPsOsrVA3YY2361GvwkGZBJ9/BpxZaRNs3ac1PPWrXV6gasMfa9KnX4CHNwkntGld4zZOPLc/a1XxDtTvUeRESIJXlySdFXVqoK/3b1XxD1YBDnVcdhBojIVFCJ9+hDi3Upf7tar6hasChzqtqQo2RkGihk+9QhxbqUv92Nd9QNeBQ51U1ocZISLRQk68T6sxkIvgdIQWgJh8Ksen+vseomjwbYnpmgpAc+MRrncy/vKWvdv85XpXu3xmjo+kC7qSOOsaomjwbAH/21fEdIY2Cd/J1Epvu73OMqsmzIbZnJgjJgXfydeOi1k0eddQ+SaG+ist6Qa6p+jtCGkXad/KudNWY9Oc6NN0UYgt5NlAXJwmRrpN3lW8cW96yy7rqJmJ7piCLPBuamqNPkiRdJ+9KV41Nf3ZZV91ECrGFPBuoi5OESFeTd6Ubx6Y/1zXfFGILeTZQFyeJkO6dfOq1XUzENl8TqdhBiGfSdfKp13YxUYcmXwexrTshgZKuk0+9touJOjT5Ooht3QkJFNauSQ3WPiGkcbB2TZOglk0I6YJOPjVS0rKrfgitSP8xPRhHCFJOoWwqHc36tk+10g2nzGg5+Ni07KqLoBXpP4XCbKRxUJMnYfL5E9tPux7ElAHgY1v99F/1nAgpCDV5Eh9VPwzlskBZqA/GEQI6eRIqeQFkF7r4RAHqrDEY1CYRQidPwsQUQB46y03hsrwAtak42tBZ6QS1SWOgkydhYnoYattGN4XL8h62MhVH27aRD2iR6CgVeBWRVQDeBuAZAI8A+AdV3d8+dhmAiwCMAbhYVW+ZqD8GXsmE1PGwFx8oI5FRZeD1VgAnqupJAH4F4LL2gLMBnA/g9QDOAfBVEekrORYh4W6KsuES4N+PAK6Y0vq94ZLi47vsizSeUk5eVTeq6nPtt5sAdP4VLARwnar+SVUfA7AdwBvKjEUIgHoe9rIdY8MlwMiVgI613utY630R5+yyL0LgVpN/H4Cb26+nA+hOKN7VbiOkHHUULrMdY/Qqu/Y8XPZFCHp44lVEfgzg6IxDy1T1xvY5ywA8B+Dazscyzs8U/0VkCYAlADA4ONjDlEnjqWNDD5sxOnfdvbbX1Rch6MHJq+ob846LyAUAFgCYry9GcXcBGOg6bQaAJwz9rwGwBmgFXnuYc9zcf338JQeaTNb1k75sJ1wkDOWyL0JQUq4RkXMAfALAuar6dNeh9QDOF5HDRGQWgCEAPy8zVhLEtik4GY/p+s08I/v8uRfaj2H6TJG+CEF5Tf4rAF4F4FYRuU9EvgYAqvoAgOsB/BLAfwP4kCr/3oxuU3AyHtP1e+pRYPiiF++2pa/1fsFq+zEWrHbXFyFggbJ6Yf513PD6kUDJy5NnqeFeKKKjZ31mygxDFUPHtU9M891wSStLQ8dad4hzL+Qdog1Frh9jMMQzLGswEUV0dJ+1T0xjX30u86/LYps/zxgMCQA6+YkooqP7rH1iGvux/8k+n/nXvWObP88YDAkAyjUT4brueNU53ra1zRkPt8Pm+rH+PAmAtO/k66g77uozrvYOtdX3fedf59kd+36qrD9PAiBdJ+9KDy1SK8Wndjt0Vnb7tBOy233mX+fZnYKendKm6iRa0nXyrvTQIrVSfGq32zZmtz/7f+HlX+fZnYKeXUedHUImIF1N3qUeWkRH96Xd5vW1YHVYKZMu4x2hUkedHUJySPdOPiY91OVcU7G7Djti1/wJ6YF0nXxMeqjLuZo0eVO7T/LmWvX1S0HzJ6QH0nXyMemhLudq0uRN7T7Jm2vV1y8FzZ+QHkhXkwfi0kNdzTWm3OyJ5lrl9YtpnQgpQbp38qlgu99nKpp8qGNTxyeRQScfMkX2+2xqLKKOsanjkwihkw+ZIvt9NjUWUcfY1PFJhKStycdO0f0+mxiLqGNs6vgkQtK+k49dPzXVlem0u7KvSD8+19Z27KrrAk2ZEf93jSRLuk4+Bf00b79PV/a5rJdfx9raju1yriYdf+is+L9rJFnSdfIp6Kd5+326ss9lvfw61tZ2bJdzNen42zbG/10jyZKuJp+KfmqqN+PKPtf18qvGdmzXc83S8W9Y4nYMQhySxp18lh4aar54Hfpw1f24XFvb9bAdu47vQajfNUKQgpP3uZ+qLXXUjbetUVNHvXwTRdbDduw6cvFjejaBNI74nbzP/VRtqaNuvG2Nmjrq5Zsosh62Y9eRix/TswmkcYiq+p7DCwwPD+vIyIjdh66YCiDLBgGu2F9+Ui5xOdeY7DaRgg2EBICIjKrqcNax+O/kY9JDm1o33kQKNhASOPE7+Zj0UJdzjcluEzHVvickUuJ38jHpoS7nGpPdJmKqfU9IpMSvyZN4oSZPiBPS1uRJ74RWoyZkTd5XfRzXfZHGQyffFEKsURNqXMFnfZwUai6RoKCTbwoh1qgJNa7gsz5OCjWXSFCkW7uGjCfUGjUh1r73WR8nlZpLJBhK3cmLyH+IyP0icp+IbBSRY7uOXSYi20XkYRE5u/xUSSl816gpgi9t2md9HN9rTpKjrFyzSlVPUtW/BLABwOUAICKzAZwP4PUAzgHwVRHTDhikFnzWqCmCT23aZ32cUOMUJFpKOXlV/UPX21fgxXy4hQCuU9U/qepjALYDeEOZsUhJfNaoKYJPbdpnfZxQ4xQkWkrnyYvIpwH8PYDfAzhTVfeJyFcAbFLVa9rnXAngZlX9XsbnlwBYAgCDg4Nzd+zYUWo+JBGYQ09Iz5TKkxeRH4vI1oyfhQCgqstUdQDAtQA+3PlYRleZ/5uo6hpVHVbV4f7+/t4sIulDbZoQJ0yYXaOqb+yxr+8AuAnAJwHsAjDQdWwGgCesZ0eay/zLWxp8t2RDbZoQa8pm1wx1vT0XwEPt1+sBnC8ih4nILABDAH5eZizSMKhNE+KEsnnyK0XkeADPA9gB4J8AQFUfEJHrAfwSwHMAPqSqYyXHIk0jxBx6QiKjlJNX1XfmHPs0gE+X6Z8QQkg5WNaAEEIShk6eEEIShk6eEEIShk6eEEISJqidoURkH1pZOkWZBuBJR9OJCdrdLGh3s+jF7teoaubTpEE5+bKIyIjp0d6Uod3NgnY3i7J2U64hhJCEoZMnhJCESc3Jr/E9AU/Q7mZBu5tFKbuT0uQJIYSMJ7U7eUIIIV3QyRNCSMJE7+Sbupm4iKwSkYfatv9ARKZ2HUvZ7neJyAMi8ryIDB90LFm7AUBEzmnbtl1ElvqeT5WIyLdEZK+IbO1qO0JEbhWRbe3fh/uco2tEZEBE7hCRB9vf8Y+028vZrapR/wD4867XFwP4Wvv1bAC/AHAYgFkAHgHQ53u+Du0+C8DL2q8/C+CzDbH7dQCOB/ATAMNd7anb3de26TgAh7Ztne17XhXa+zcATgGwtavtcwCWtl8v7XznU/kBcAyAU9qvXwXgV+3vdSm7o7+T14ZuJq6qG1X1ufbbTWjtvgWkb/eDqvpwxqGk7UbLlu2q+qiqPgPgOrRsThJVvRPAUwc1LwRwdfv11QAW1TmnqlHVPap6T/v1HwE8CGA6StodvZMHWpuJi8hOAO8B0NkfbjqAnV2n7Wq3pcj7ANzcft0ku7tJ3e7U7euFo1R1D9ByiACO9DyfyhCRmQBOBrAZJe0uuzNULYjIjwEcnXFomareqKrLACwTkcvQ2kz8k7DYTDxUJrK7fc4ytHbfurbzsYzzk7M762MZbVHZPQGp20faiMgrAXwfwEdV9Q8iWZe+d6Jw8trQzcQnsltELgCwAMB8bQt2aIDdBqK3ewJSt68Xfisix6jqHhE5BsBe3xNyjYhMQsvBX6uqN7SbS9kdvVzT1M3EReQcAJ8AcK6qPt11KGm7c0jd7rsBDInILBE5FMD5aNncJNYDuKD9+gIApr/qokRat+xXAnhQVVd3HSpnt++IsoOI9PcBbAVwP4AfApjedWwZWhkJDwN4s++5OrZ7O1oa7X3tn681xO63o3VX+ycAvwVwSxPsbtv3FrQyLh5BS7ryPqcKbV0LYA+AZ9vX+yIArwZwG4Bt7d9H+J6nY5vPQEuCu7/r3/VbytrNsgaEEJIw0cs1hBBCzNDJE0JIwtDJE0JIwtDJE0JIwtDJE0JIwtDJE0JIwtDJE0JIwvw/tUIfNpAzL5IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot both distributions.  A distribution is blue, B distribution is orange.\n",
    "plt.scatter(x_A,y_A)\n",
    "plt.scatter(x_B,y_B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Join x and y data.\n",
    "all_x = np.concatenate((x_A,x_B), axis=0)\n",
    "all_y = np.concatenate((y_A,y_B), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 13,  19,   7,  -5, -10,   7,  12,   0,  -9,  13,   2,  11,   9,\n",
       "         -2,  -5,   7,  -2,   5,  15,  12,   3,  10,  12,  -3,  12,  -6,\n",
       "          2,  16,  -7,  13,  16,  -9,  16,  -7,  -8,   7,  -5,   3,  -6,\n",
       "         -7,  -4,  -9,  -1,  -9,  15,   7,  -6,  -2,  -9,  -1,  -5,  -8,\n",
       "          5,  -7,  -1,  -2,   6,  16,  -6,   1,  17,  12,  -9,   1,  -1,\n",
       "          5,  12,  11,   2,  -3,  -6,  13,  10,  19,  13,   4,   0,  19,\n",
       "         -5,   7,   3,   2,  13,  -6,   6,   3,  -7,  -1,  12,  -1,  -4,\n",
       "          0,   8,  14,  -8,  17,  17,   2,   9,   4,  -9,  -5,  -1, -21,\n",
       "        -23, -19, -21, -18,  -4, -19,  -9, -19, -11, -22, -19, -12, -20,\n",
       "        -29, -23,  -1, -17, -26, -20, -26, -12, -10,  -2, -12,  -1,  -5,\n",
       "        -24, -11, -29, -21, -16,  -9,  -3, -23, -29, -12, -19, -20, -12,\n",
       "        -11, -26,  -1, -18, -14,  -3, -15, -18, -27, -23, -26,  -2,  -4,\n",
       "         -7, -19, -25, -22, -30, -21, -22,  -6, -17,  -4,  -5, -12, -15,\n",
       "         -8,  -6, -11,  -6,  -8,  -5, -30,  -7, -19,  -1,  -7, -20, -29,\n",
       "        -10, -21, -28, -16, -29,  -4, -25,  -2, -21, -11,  -9, -25, -13,\n",
       "        -11, -29,  -1,  -3,  -6]),\n",
       " array([ 15,  11,  15,  -9,  16,  -1,   0,  17,  17,   6,  -6,  16,   1,\n",
       "         -9,   2,   7,   2,  13,  18,   3,  17,  18,   0,  -2,  -5,  -4,\n",
       "         11,  15,   6,  15,  11,  -8, -10,  -4,   5,  -2,  -8,  -6,  -2,\n",
       "          9,   9,  11,  15,  10,  -9,  13,   0,  -2,  17,   4,  13,   7,\n",
       "         14,  -2,  19,  -4,   7,  14,   8,  -2,  16,  19,   9,   9,  16,\n",
       "         14,  12,   8,  -4,  -1,  -3,  13,   3,  -6,  -3,  14,   7,  -6,\n",
       "          3,   8,   7,  10,  -2,  -8,  13,  -5,  -4,  19,  -9,   8,  18,\n",
       "         14,   9,  14,  -4,  19,   4,   5,   8,  -9, -12, -18, -20, -25,\n",
       "        -21, -12,  -2, -22, -29, -24, -11, -15, -13, -12, -17, -27, -12,\n",
       "         -8, -27, -29, -26, -16, -13, -22, -24, -19,  -2, -28, -20, -21,\n",
       "        -22, -30,  -3, -10,  -3,  -1,  -1, -14,  -4,  -8, -23, -26, -23,\n",
       "        -23,  -2,  -3, -29,  -9, -22, -26, -22, -13,  -1,  -2,  -4, -20,\n",
       "        -15, -22, -11, -23, -27, -25,  -7, -19, -13, -20, -25,  -3, -15,\n",
       "        -26, -29, -18, -15,  -7,  -2,  -5, -24, -16, -14, -18, -27, -23,\n",
       "        -26,  -8,  -3, -29, -26, -26,  -2, -10,  -4,  -6,  -9,  -2,  -6,\n",
       "        -16, -16, -12, -10, -12]))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_x, all_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ 13,  15],\n",
       "        [ 19,  11],\n",
       "        [  7,  15],\n",
       "        [ -5,  -9],\n",
       "        [-10,  16],\n",
       "        [  7,  -1],\n",
       "        [ 12,   0],\n",
       "        [  0,  17],\n",
       "        [ -9,  17],\n",
       "        [ 13,   6],\n",
       "        [  2,  -6],\n",
       "        [ 11,  16],\n",
       "        [  9,   1],\n",
       "        [ -2,  -9],\n",
       "        [ -5,   2],\n",
       "        [  7,   7],\n",
       "        [ -2,   2],\n",
       "        [  5,  13],\n",
       "        [ 15,  18],\n",
       "        [ 12,   3],\n",
       "        [  3,  17],\n",
       "        [ 10,  18],\n",
       "        [ 12,   0],\n",
       "        [ -3,  -2],\n",
       "        [ 12,  -5],\n",
       "        [ -6,  -4],\n",
       "        [  2,  11],\n",
       "        [ 16,  15],\n",
       "        [ -7,   6],\n",
       "        [ 13,  15],\n",
       "        [ 16,  11],\n",
       "        [ -9,  -8],\n",
       "        [ 16, -10],\n",
       "        [ -7,  -4],\n",
       "        [ -8,   5],\n",
       "        [  7,  -2],\n",
       "        [ -5,  -8],\n",
       "        [  3,  -6],\n",
       "        [ -6,  -2],\n",
       "        [ -7,   9],\n",
       "        [ -4,   9],\n",
       "        [ -9,  11],\n",
       "        [ -1,  15],\n",
       "        [ -9,  10],\n",
       "        [ 15,  -9],\n",
       "        [  7,  13],\n",
       "        [ -6,   0],\n",
       "        [ -2,  -2],\n",
       "        [ -9,  17],\n",
       "        [ -1,   4],\n",
       "        [ -5,  13],\n",
       "        [ -8,   7],\n",
       "        [  5,  14],\n",
       "        [ -7,  -2],\n",
       "        [ -1,  19],\n",
       "        [ -2,  -4],\n",
       "        [  6,   7],\n",
       "        [ 16,  14],\n",
       "        [ -6,   8],\n",
       "        [  1,  -2],\n",
       "        [ 17,  16],\n",
       "        [ 12,  19],\n",
       "        [ -9,   9],\n",
       "        [  1,   9],\n",
       "        [ -1,  16],\n",
       "        [  5,  14],\n",
       "        [ 12,  12],\n",
       "        [ 11,   8],\n",
       "        [  2,  -4],\n",
       "        [ -3,  -1],\n",
       "        [ -6,  -3],\n",
       "        [ 13,  13],\n",
       "        [ 10,   3],\n",
       "        [ 19,  -6],\n",
       "        [ 13,  -3],\n",
       "        [  4,  14],\n",
       "        [  0,   7],\n",
       "        [ 19,  -6],\n",
       "        [ -5,   3],\n",
       "        [  7,   8],\n",
       "        [  3,   7],\n",
       "        [  2,  10],\n",
       "        [ 13,  -2],\n",
       "        [ -6,  -8],\n",
       "        [  6,  13],\n",
       "        [  3,  -5],\n",
       "        [ -7,  -4],\n",
       "        [ -1,  19],\n",
       "        [ 12,  -9],\n",
       "        [ -1,   8],\n",
       "        [ -4,  18],\n",
       "        [  0,  14],\n",
       "        [  8,   9],\n",
       "        [ 14,  14],\n",
       "        [ -8,  -4],\n",
       "        [ 17,  19],\n",
       "        [ 17,   4],\n",
       "        [  2,   5],\n",
       "        [  9,   8],\n",
       "        [  4,  -9],\n",
       "        [ -9, -12],\n",
       "        [ -5, -18],\n",
       "        [ -1, -20],\n",
       "        [-21, -25],\n",
       "        [-23, -21],\n",
       "        [-19, -12],\n",
       "        [-21,  -2],\n",
       "        [-18, -22],\n",
       "        [ -4, -29],\n",
       "        [-19, -24],\n",
       "        [ -9, -11],\n",
       "        [-19, -15],\n",
       "        [-11, -13],\n",
       "        [-22, -12],\n",
       "        [-19, -17],\n",
       "        [-12, -27],\n",
       "        [-20, -12],\n",
       "        [-29,  -8],\n",
       "        [-23, -27],\n",
       "        [ -1, -29],\n",
       "        [-17, -26],\n",
       "        [-26, -16],\n",
       "        [-20, -13],\n",
       "        [-26, -22],\n",
       "        [-12, -24],\n",
       "        [-10, -19],\n",
       "        [ -2,  -2],\n",
       "        [-12, -28],\n",
       "        [ -1, -20],\n",
       "        [ -5, -21],\n",
       "        [-24, -22],\n",
       "        [-11, -30],\n",
       "        [-29,  -3],\n",
       "        [-21, -10],\n",
       "        [-16,  -3],\n",
       "        [ -9,  -1],\n",
       "        [ -3,  -1],\n",
       "        [-23, -14],\n",
       "        [-29,  -4],\n",
       "        [-12,  -8],\n",
       "        [-19, -23],\n",
       "        [-20, -26],\n",
       "        [-12, -23],\n",
       "        [-11, -23],\n",
       "        [-26,  -2],\n",
       "        [ -1,  -3],\n",
       "        [-18, -29],\n",
       "        [-14,  -9],\n",
       "        [ -3, -22],\n",
       "        [-15, -26],\n",
       "        [-18, -22],\n",
       "        [-27, -13],\n",
       "        [-23,  -1],\n",
       "        [-26,  -2],\n",
       "        [ -2,  -4],\n",
       "        [ -4, -20],\n",
       "        [ -7, -15],\n",
       "        [-19, -22],\n",
       "        [-25, -11],\n",
       "        [-22, -23],\n",
       "        [-30, -27],\n",
       "        [-21, -25],\n",
       "        [-22,  -7],\n",
       "        [ -6, -19],\n",
       "        [-17, -13],\n",
       "        [ -4, -20],\n",
       "        [ -5, -25],\n",
       "        [-12,  -3],\n",
       "        [-15, -15],\n",
       "        [ -8, -26],\n",
       "        [ -6, -29],\n",
       "        [-11, -18],\n",
       "        [ -6, -15],\n",
       "        [ -8,  -7],\n",
       "        [ -5,  -2],\n",
       "        [-30,  -5],\n",
       "        [ -7, -24],\n",
       "        [-19, -16],\n",
       "        [ -1, -14],\n",
       "        [ -7, -18],\n",
       "        [-20, -27],\n",
       "        [-29, -23],\n",
       "        [-10, -26],\n",
       "        [-21,  -8],\n",
       "        [-28,  -3],\n",
       "        [-16, -29],\n",
       "        [-29, -26],\n",
       "        [ -4, -26],\n",
       "        [-25,  -2],\n",
       "        [ -2, -10],\n",
       "        [-21,  -4],\n",
       "        [-11,  -6],\n",
       "        [ -9,  -9],\n",
       "        [-25,  -2],\n",
       "        [-13,  -6],\n",
       "        [-11, -16],\n",
       "        [-29, -16],\n",
       "        [ -1, -12],\n",
       "        [ -3, -10],\n",
       "        [ -6, -12]]),\n",
       " (200, 2))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unify joined data.\n",
    "all_data = np.vstack((all_x, all_y)).T\n",
    "all_data, all_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1.,\n",
       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]),\n",
       " (200,))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create labels.  A distribution = 0 (blue), B distribution = 1 (orange)\n",
    "labels = np.concatenate((np.zeros(length),np.ones(length)),axis=0)\n",
    "labels, labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create tree classifier object.\n",
    "clf = tree.DecisionTreeClassifier(random_state=0,max_depth=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train classifier tree.\n",
    "fitted = clf.fit(all_data, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x116d26f50>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Create and plot test data.  Results should be 0, 1, 0, ?, ?, 1\n",
    "test_data = np.array([[13,5],[-12,-7],[100,100],[100,-100],[-100,100],[-100,-100]])\n",
    "x_test,y_test = test_data.T\n",
    "x_test,y_test\n",
    "plt.scatter(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1., 0., 0., 1., 1.])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict\n",
    "fitted.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.92523364, 0.07476636],\n",
       "       [0.        , 1.        ],\n",
       "       [0.92523364, 0.07476636],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check probabilities (try changing max_depth of tree and see how it affects probabilities)\n",
    "fitted.predict_proba(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(167.4, 181.2, 'X[1] <= -9.5\\ngini = 0.5\\nsamples = 200\\nvalue = [100, 100]'),\n",
       " Text(83.7, 108.72, 'X[0] <= 7.5\\ngini = 0.027\\nsamples = 73\\nvalue = [1, 72]'),\n",
       " Text(41.85, 36.23999999999998, 'gini = 0.0\\nsamples = 72\\nvalue = [0, 72]'),\n",
       " Text(125.55000000000001, 36.23999999999998, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
       " Text(251.10000000000002, 108.72, 'X[0] <= -10.5\\ngini = 0.344\\nsamples = 127\\nvalue = [99, 28]'),\n",
       " Text(209.25, 36.23999999999998, 'gini = 0.0\\nsamples = 20\\nvalue = [0, 20]'),\n",
       " Text(292.95, 36.23999999999998, 'gini = 0.138\\nsamples = 107\\nvalue = [99, 8]')]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Take a look at the automatically created tree and the separator nodes\n",
    "tree.plot_tree(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "cross_val_score?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.975, 0.9  , 0.95 , 0.95 , 0.925])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(clf, all_data, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:ml]",
   "language": "python",
   "name": "conda-env-ml-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}