09. Gradient Boosting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient Boosting Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gradient Boosting Regression\n",
    "\n",
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# Importing the dataset\n",
    "dataset = pd.read_csv('Data/Regression/Company_Performance.csv')\n",
    "X = dataset.iloc[:, 0:1].values\n",
    "y = dataset.iloc[:, 1].values\n",
    "\n",
    "# Splitting the dataset into the Training set and Test set\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, ccp_alpha=0.0, criterion='friedman_mse',\n",
       "                          init=None, learning_rate=0.01, loss='ls', max_depth=6,\n",
       "                          max_features=None, max_leaf_nodes=None,\n",
       "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                          min_samples_leaf=1, min_samples_split=2,\n",
       "                          min_weight_fraction_leaf=0.0, n_estimators=500,\n",
       "                          n_iter_no_change=None, presort='deprecated',\n",
       "                          random_state=None, subsample=1.0, tol=0.0001,\n",
       "                          validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "gb_r = GradientBoostingRegressor(n_estimators = 500, max_depth = 6, learning_rate = 0.01)\n",
    "gb_r.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999568287525894"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb_r.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.862517348713784"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb_r.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zs3PUdhRDts7i8RGxQtKxwGUR8XVJ9+UdmJlZRfX0JD+33nrIou3t7aP6g7+/LE1D60vaCvgIcEPO8ZiZVccwEkGtyZIIzgRuAv4REbMkbQvMzTcsM7MKq+NEMGTTUERcC1xb9PxR4F/zDMrMrOIKiWCIPoJalKWzeHtJt0i6P32+q6Sv5h+amVkFLV4M48bBJptUO5KKy9I09N/A6cArABFxH3BknkGZmVVcT0/SLJRMnVtXsiSCxoj4n37rVg71IkmXSlpSqEkMsF2SvitpnqT7JL01S8BmZrkoJII6lCURPClpOyAAJB0GPJ7hdZcDU0tsfz8wJV06gB9k2KeZWT4KN5PVoSz3EZwAdAI7SloMPAZ8bKgXRcTtklpLFDkE+Ekkgx3dJWkzSVtFRJYkY2ZWPhF1XSPIctXQo8D+ksYBDRHxbJmOPRFYWPR8UbpujUQgqYOk1kBzc3OZDm9mlnr6aXjxxbpNBFmuGtpC0o+A6yLiWUk7SzqmDMceqEdmwKFQI6IzItoioq2pqakMhzYzK1LH9xBAtj6Cy0luKCu8Q48AXyjDsRcBk4ueTwJ6yrBfM7PhqeN7CCBbIpgQEdeQDLJHRKwEVpXh2NcD/5ZePbQnsNz9A2ZWFXVeI8jSWfy8pNfz6lVDewLLh3qRpKuAfYEJkhYBXwc2AIiIi4GZwAeAeUAv8Mm1iN/MbN0tXpz83Gqr6sZRJVkSwSkk3963k/RXoAk4bKgXRcRRQ2wPkiuSzMyqq6cHNt8cxo6tdiRVkeWqoTmS3gXsQNLB+3BEvJJ7ZGZmlVLHl45CtquGZpNcutkTEfc7CZhZzXEiGNKRJNf3z5J0taT3SXU4GIeZ1S4ngtIiYl5ETAO2B64ELgUWSDojnb7SzGz0WrUKHn+8bi8dhYyT16cT1p8HnAv8nKSzeAXwx/xCMzOrgKVLk2TgGsHgJM0BzgdmAbtGxOci4m8RcR7waN4BmpkNpauri9bWVhoaGmhtbaWrqyv7i+v8HgLIdvno4el4Q2uIiEPLHI+Z2bB0dXXR0dFBb28vAN3d3XR0dABkm2DeiSBTIlgm6dvAO9PnfwLOjIghbyozM1snTz8Nl1wCL700aJEl55/Pl9IksFpvL0tOOAHmzRv6GHffnfys4z6CLIngUuB+4CPp848DlwGuDZhZvn75Szj99JJFvjjYhuXLYfr0bMfZZhvYYovhRFZTsiSC7SKieLL6MyTdk1dAZmarLU8bHpYtg9e9bsAira2tdC9YsMb6luZm5s+fn/1YdXxVfJarhl6Q9PbCE0n7AC/kF5KZWerZdPqTTTdNPqgHWGacfTaNjY2veVljYyMzzj570NcMuNSxLDWCzwA/ljSeZIiJp4Cj8wzKzAxIEsGYMbD+4B9VhQ7hadOmsWDBApqbm5kxY0a2jmIDso01dA+wm6RN0+crco/KzAySRLDJJkMWa29v9wf/Ohg0EUg6eZD1AETEt3OKycwskTER2LopVSPwu29m1fXcc04EFTBoIoiIMyoZiJnZGp59FjbeuNpR1LwsQ0xsK+k3kpZKWiLp15K2rURwZlbn3DRUEVkuH70SuAbYimQC+2uBq/IMyswMcCKokCyJQBHx04hYmS5XkM5fPOQLpamSHpY0T9JpA2xvlnSrpLsl3SfpA8M9ATOrYU4EFZElEdwq6TRJrZJaJH0ZuFHS5qXmI5C0HvB94P3AzsBRknbuV+yrwDUR8RaSCXAuWrvTMLOa5M7iishyQ9kR6c/j+q3/FEnNYLD+grcB8wojl0q6GjgEeLCoTACbpo/HAz0Z4jGzehCRJAJ3Fucuyw1l26zlvicCC4ueLwL+pV+Z6cDvJZ0EjAP2H2hHkjpI5k2mubl5LcMxs1Gltxf6+lwjqIAhE0HaxHMg0FpcPsMNZQMN3tG/b+Eo4PKIOE/SXsBPJe0SEX2veVFEJ9AJ0NbWlql/wsxGucI4Q04EucvSNPQb4EXg70DfEGWLLQImFz2fxJpNP8cAUwEi4k5JY4AJwJJhHMfMapETQcVkSQSTImLXtdj3LGCKpG2AxSSdwR/tV2YBsB9wuaSdgDHA0rU4lpnVmueeS346EeQuy1VDv5X03uHuOCJWAicCNwEPkVwd9ICkMyUdnBY7Bfi0pHtJ7k04OiLc9GNmrhFUUJYawV3ALyU1AK+QtP1HRGxa+mUQETOBmf3Wfa3o8YPAPsOK2MzqQyER+Kqh3GVJBOcBewF/97d1M6sY1wgqJkvT0FzgficBM6soJ4KKyVIjeBy4TdJvgZcKKz0fgZnlyp3FFZMlETyWLhumi5lZ/txHUDFZ7iw+A0DSuIh4Pv+QzMxIEsHYsSXnK7byyDIfwV6SHiS5BBRJu0ny4HBmli9PSlMxWTqLLwDeBywDiIh7gXfmGZSZmYegrpwsiYCIWNhv1aocYjEze5WHoK6YLI1vCyXtDYSkDYHPkTYTmZnlxjWCislSIzgeOIFkWOlFwO7pczOz/DgRVEyWq4aeBNorEIuZ2auefRa2WdvpUGw4MvURmJlVnGsEFeNEYGYjkzuLK2bQRCDp8+lPjw5qZpVVmK/YiaAiStUIPpn+vLASgZiZreb5iiuqVGfxQ5LmA02S7itaX5iPYG1mLTMzG5pHHq2oQRNBRBwlaUuSGcYOHqycmVnZecC5iip5+WhEPAHslt5Itn26+uGIeCX3yMysfnkI6orKMujcu0gmp/k+cBHwiKRMYw1JmirpYUnzJJ02SJmPSHpQ0gOSrhxO8GZWo9w0VFFZhpj4NvDeiHgYQNL2JBPN71HqRZLWI0keB5DckTxL0vXpPMWFMlOA04F9IuJpSW9Yu9Mws5riRFBRWe4j2KCQBAAi4hFggwyvexswLyIejYiXgauBQ/qV+TTw/Yh4Ot33kmxhm1lNcyKoqCyJYLakH0naN13+G5iT4XUTgeJRSxel64ptD2wv6a+S7pI0NVvYZlbT3FlcUVmahj5DMsjc50guHb2dpK9gKBpgXQxw/CnAvsAk4M+SdomIZ16zI6kD6ABobm7OcGgzG9XcWVxRWQade4mkn2C4k9UvAiYXPZ8E9AxQ5q70KqTHJD1Mkhhm9YuhE+gEaGtr659MzKzWuEZQUXmONTQLmCJpm/Ty0yOB6/uV+RXwbgBJE0iaih7NMSYzGw08X3FF5ZYIImIlcCLJDWkPAddExAOSzpRUuEHtJmBZOifyrcCpEbEsr5jMbJTwyKMVlWu6jYiZwMx+675W9DiAk9PFzCzhRFBRa1UjSDtvzczy8dxz7h+ooLVtGhroiiAzs/JwjaCi1ioRRMQl5Q7EzGw1J4KKyjLW0CRJv5S0VNI/Jf1c0qRKBGdmdcqJoKKy1AguI7nscyuSO4N/k64zM8uHE0FFZUkETRFxWUSsTJfLgaac4zKzeuZpKisqSyJ4UtLHJK2XLh8DfK2/mZXU1dVFa2srDQ0NtLa20tXVle2FhfmKfdVQxWS5j+BTwPeA80nGCrojXWdmNqCuri46Ojro7e0FoLu7m46O5Krz9vb20i/2fMUVl2WsoQV4qkozK+jpgaVLSxa54tRTeWOaBFbr7eWKU0+lfZddSu//qaeSn04EFTNoIpD0tcG2kdwUfFYO8ZjZSNbbC298I7zwQslivx1sw+OPw+67ZztWk7siK6VUjeD5AdaNA44BXg84EZjVm8WLkyTwxS/CO94xaLHjjjuOJQPUGt7Q1MQll2S4DWnMGNh//3WJ1IZh0EQQEecVHkvaBPg88EmSmcbOG+x1ZlbDetKR5A88EPbbb9Bi7+ztfU0fAUBjYyOd558PH/5w3lHaMJW8akjS5pK+AdxHkjTeGhFf8ZSSZnVq8eLk58T+kw2+Vnt7O52dnbS0tCCJlpYWOjs7h+4otqoo1UdwLnAoyYQwb46I5yoWlZmNTIUawdZbD1m0vb3dH/yjRKkawSnA1sBXgR5JK9LlWUkrKhOemY0oPT0wbpyv6KkxpfoI8py9zMxGo56epDYgD0BcS/xhb2bZLV48ZP+AjT5OBGaWXaFGYDXFicDMsolwIqhRuSYCSVMlPSxpnqTTSpQ7TFJIasszHjNbB888Ay++6ERQg3JLBJLWA74PvB/YGThK0s4DlNsE+Bzwt7xiMbMyKFw66j6CmpNnjeBtwLyIeDQiXia5I/mQAcqdBZwDvJhjLGa2rgo3k7lGUHPyTAQTgYVFzxel61aT9BZgckTcUGpHkjokzZY0e+kQox6aWU6GcTOZjS55JoKBLjSO1RulBpI5Dk4ZakcR0RkRbRHR1uQRCc2qo5AIttqqunFY2eWZCBYBk4ueTwJ6ip5vAuwC3CZpPrAncL07jM1GqJ4eeN3rYOzYakdiZZZnIpgFTJG0jaQNgSOB6wsbI2J5REyIiNaIaAXuAg6OiNk5xmRma8s3k9Ws3BJBRKwETgRuAh4CromIBySdKckznpmNNr6HoGZlmbN4rUXETGBmv3UDznwWEfvmGYuZraOeHth5jSvArQb4zmIzG1pfXzLNpGsENcmJwMyGtmQJrFrlPoIa5URgZkPzPQQ1zYnAzIbmRFDTnAjMbGhOBDXNicDMhrZ4cTIr2ZZbVjsSy4ETgVmd6OrqorW1lYaGBlpbW+nq6sr+4p4e2GILWD/XK86tSvxbNRspVq6Ehx9OJoApsxtuuIELzjiDjV98kZ0Buru54NhjGb9wIQcddNDQO5g7181CNUyRwx9dntra2mL2bI9CYTVo+nQ444xqRzG4Qw+Fn/+82lHYWpI0JyIGHMvNNQKzkeLuu2GbbeCcc8q+68MPP5yBvvIJuPbaa7PtZO+9yxmSjSBOBGYjxSOPwO67w2GHlX3Xs1pa6O7uXmN9S0tLLsez0cWdxWYjwapV8I9/wPbb57L7GTNm0NjY+Jp1jY2NzJgxI5fj2ejiRGA2EnR3wyuvwJQpuey+vb2dzs5OWlpakERLSwudnZ20t7fncjwbXdw0ZDYSzJ2b/MypRgBJMvAHvw3ENQKzkeCRR5KfOdUIzEpxIjAbCebOhY03Tm7aMqswJwKzkeCRR5JmIanakVgdciIwGwnmznWzkFVNrolA0lRJD0uaJ+m0AbafLOlBSfdJukVSS57xmI1IL78M8+fn2lFsVkpuiUDSesD3gfcDOwNHSeo/4endQFtE7ApcB5T/lkqzEaowCNxOG20EfX3csXRptUOyOpVnjeBtwLyIeDQiXgauBg4pLhARt0ZEb/r0LmBSjvGYjRhdXV10dHTQ3d3NG9N1p1922fBGBDUrkzwTwURgYdHzRem6wRwD/HagDZI6JM2WNHupvzVZDZg2bRq9vcl3oEKD0N9feolp06ZVLyirW3kmgoEufxhwqFNJHwPagHMH2h4RnRHRFhFtTU1NZQzRrDoWLFiw+vEU4Eng6X7rzSolz0SwCJhc9HwS0NO/kKT9gWnAwRHxUo7xmI0Yzc3Nqx9vD8wdYL1ZpeSZCGYBUyRtI2lD4Ejg+uICkt4CXEKSBJbkGIvZiFI8CNwU4BE8CJxVT26JICJWAicCNwEPAddExAOSzpR0cFrsXGBj4FpJ90i6fpDdmVXEOk3nOAyFQeB2mDyZycDS8eM9CJxVjWcos9Fl4UJ47LFcdn3zzTdz7rnn8uJLr7ZQjtloI0499VQOOOCAXI7J/PnwiU/Az34GH/lIPscwo/QMZU4ENnr09cHkyclE6rXmgQdg5/632ZiVj6eqtNowe3aSBKZPh3e8o+y732+//QadzvGWW24p+/FW22wzJwGrKicCG9G6urqYNm0aCxYs4PxNN+UkiYYTT4TXv77sx/pHqekc3/Oesh/PbKTwoHM2YhXffRsR7LN8OX+T6Prd73I5nqdztHrlRGAjVvHdt1uS3HH4m76+3O6+9XSOVq/cWWwjVkNDA4W/z08ClwK7AX+X6Ovrq2ZoZqNOqc5i1whsxCq+y/ZAkoGr7sN335qVmxOBDUulbriCV9vsNwAOAGbiNnuzPPiqIcus0HlbaLfv7u6mo6MDIJd29MI+bzz5ZDZdsoRZTU10nn++2+zNysx9BJa45Zbk+vyXBh/375577+Xll19eY/2GG27I7rvtll9sTzwBS5bAsmUwblx+xzGrYb6hrMYVX2vf3NzMjBkzhvet+U9/gpL8QW0AAAhJSURBVA9+ELbcEnbccdBiPQMkAQBefpndJ0wYZtTDMGEC7Luvk4BZXiJiVC177LFHjHRXXHFFtLS0hKRoaWmJK664ItdjNTY2BslcDwFEY2Nj9mPeeWfExhtH7LRTxJIlJYu2tLS85jiFpaWlZd1PxMxyBcyOQT5Xq/7BPtxlbRLBqPpg7uuLuPvuiC99KWK77SKamkouSxsa4p+wxrK0oWHI10ZTU8QGG0S88Y0RPT35n5uZVU2pRFDzfQSFDs6+3l42T9eNHTOGc845h0MPPTTbThYuhBtvhBtugAGGICj2zPLlA17j3tDQwGbjxw99rL4+WL4c1l8f3vteaGkpWfwHP/jBoOPjfOYznxn6eGPHwhe+kAzmlsE6N0OZWVXU9eijra2tdHd3cxhw7bocuKEB3v522HVX0ECzcCYuvPDCQT+YTzrppGzH2mknOPzwpG18CIXz66+lpYX58+dnO56Z1by6TgSFu1O3AfYrWi+gs7Mz20422wz22w8233zIopX+YO5/SSck19p7aAQzK1YqEVS9zX+4y3D7CCrdwVmNdvRK9oGY2ehEiT6Cmr+zuNIjSlZj4LL29nbmz59PX18f8+fPd03AzIYl16YhSVOB7wDrAT+MiG/2274R8BNgD2AZcEREzC+1z7W5ocwdnGZW76rSRyBpPeARkmFiFgGzgKMi4sGiMp8Fdo2I4yUdCXw4Io4otV/fWWxmNnzVGn30bcC8iHg0Il4GrgYO6VfmEODH6ePrgP2kEpfkmJlZ2eWZCCaSjBxcsChdN2CZiFgJLAfWmINQUoek2ZJmL126NKdwzczqU56JYKBv9v3bobKUISI6I6ItItqamprKEpyZmSXyTASLgOLbVScBPYOVkbQ+MB54KseYzMysnzwTwSxgiqRtJG0IHAlc36/M9cAn0seHAX+MPC9jMjOzNeR9+egHgAtILh+9NCJmSDqT5MaG6yWNAX4KvIWkJnBkRDw6xD6XAqUH/Bk5JgBPVjuInNTyuUFtn5/PbfRal/NriYgB29ZH3RATo4mk2YNdrjXa1fK5QW2fn89t9Mrr/Gr+zmIzMyvNicDMrM45EeQr4/Cmo1ItnxvU9vn53EavXM7PfQRmZnXONQIzszrnRGBmVuecCHIgabKkWyU9JOkBSZ+vdkzlJmk9SXdLuqHasZSTpM0kXSfp/9Lf317VjqmcJH0x/Zu8X9JV6b08o5KkSyUtkXR/0brNJd0saW7683XVjHFdDHJ+56Z/m/dJ+qWkzcpxLCeCfKwETomInYA9gRMk7VzlmMrt88BD1Q4iB98BfhcROwK7UUPnKGki8DmgLSJ2IbnR88jqRrVOLgem9lt3GnBLREwBbkmfj1aXs+b53QzsEhG7kgzzf3o5DuREkIOIeDwi/jd9/CzJh0n/kVdHLUmTgAOBH1Y7lnKStCnwTuBHABHxckQ8U92oym59YGw6tlcja47/NWpExO2sOTZZ8dD2PwY+VNGgymig84uI36cjNQPcRTKG2zpzIsiZpFaSITT+Vt1IyuoC4MtAX7UDKbNtgaXAZWmz1w8ljat2UOUSEYuB/wIWAI8DyyPi99WNquy2iIjHIflCBryhyvHk6VPAb8uxIyeCHEnaGPg58IWIWFHteMpB0kHAkoiYU+1YcrA+8FbgBxHxFuB5RnfTwmuk7eWHANsAWwPjJH2sulHZ2pA0jaQJuqsc+3MiyImkDUiSQFdE/KLa8ZTRPsDBkuaTzDr3HklXVDekslkELIqIQu3tOpLEUCv2Bx6LiKUR8QrwC2DvKsdUbv+UtBVA+nNJleMpO0mfAA4C2ss1WrMTQQ7S6TZ/BDwUEd+udjzlFBGnR8SkiGgl6Wj8Y0TUxLfKiHgCWChph3TVfsCDJV4y2iwA9pTUmP6N7kcNdYanioe2/wTw6yrGUnaSpgJfAQ6OiN5y7deJIB/7AB8n+bZ8T7p8oNpBWSYnAV2S7gN2B86ucjxlk9Z0rgP+F/g7yf//qB2SQdJVwJ3ADpIWSToG+CZwgKS5wAHp81FpkPP7HrAJcHP6uXJxWY7lISbMzOqbawRmZnXOicDMrM45EZiZ1TknAjOzOudEYGZW55wIrC5JCknnFT3/kqTpa7GfN0h6TNKWResuklQzdyRb7XMisHr1EnCopAnrspOIWAJ8i2QMHyS9FXg7cF6p15WSDghnVjFOBFavVpLcTPXF/hsktUi6JR3z/RZJzUPsqxPYTtK7SW74ORFolvQ7SXMk/VnSjum+Pyjpb+mgdn+QtEW6frqkTkm/B34i6U2S/ie9aeg+SVPKefJmxZwIrJ59H2iXNL7f+u8BP0nHfO8CvltqJxHRB3yGZGypR9LhgzuBkyJiD+BLwEVp8b8Ae6aD2l1NMoprwR7AIRHxUeB44DsRsTvQRjIOklkuXAW1uhURKyT9hGSylheKNu0FHJo+/ilwToZ93ZPOJHVROurs3sC1yZA+AGyU/pwE/CwdEG1D4LGi3VwfEYU47gSmpXM//CIi5g77BM0yco3A6t0FwDFAqXkHso7D0pcuDcAzEbF70bJTWuZC4HsR8WbgOKB4qsjnVx8w4krgYJIEdZOk92SMwWzYnAisrkXEU8A1JMmg4A5encKxnaQ5Zzj7XAE8JulwSEajlbRbunk8sDh9/ImBXp++Zlvg0Yj4LsmImrsOJwaz4XAiMEuu8Cm+euhzwCfTEUg/TjI/M5KOl3R8xn22A8dIuhd4gGRCGIDpJE1GfwaeLPH6I4D7Jd0D7Aj8JONxzYbNo4+amdU51wjMzOqcE4GZWZ1zIjAzq3NOBGZmdc6JwMyszjkRmJnVOScCM7M69/8BruTJBPPADt4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the Random Forest Regression results (higher resolution)\n",
    "#Graph \n",
    "X_dt = np.arange(min(X), max(X), 0.1)\n",
    "X_dt = X_dt.reshape(len(X_dt), 1)\n",
    "plt.scatter(X, y, color = 'black')\n",
    "plt.plot(X_dt, gb_r.predict(X_dt), color = 'red' )\n",
    "plt.title('Gradient Boosting Regression')\n",
    "plt.ylabel('No. of employees')\n",
    "plt.xlabel('No. Years')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient Boosting Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9416909620991254\n"
     ]
    }
   ],
   "source": [
    "#Import Libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "dataset = pd.read_csv('Data/Classification/BankNote_Authentication.csv')\n",
    "\n",
    "X = dataset.iloc[:, [0,1]].values\n",
    "y = dataset.iloc[:, 4].values\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)\n",
    "\n",
    "# Fitting Random Forest Regression to the dataset\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "gb_c = GradientBoostingClassifier(n_estimators = 200, random_state = 0)\n",
    "gb_c.fit(X_train, y_train)\n",
    "\n",
    "# Predicting the Test set results\n",
    "from sklearn.metrics import accuracy_score\n",
    "y_pred = gb_c.predict(X_test)\n",
    "test_acc = accuracy_score(y_test, y_pred)\n",
    "print(test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "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": [
    "from matplotlib.colors import ListedColormap\n",
    "import numpy as np\n",
    "#Define Variables\n",
    "clf = gb_c\n",
    "h = 0.01\n",
    "X_plot, z_plot = X_test, y_test\n",
    "\n",
    "#Standard Template to draw graph\n",
    "x_min, x_max = X_plot[:, 0].min() - 1, X_plot[:, 0].max() + 1\n",
    "y_min, y_max = X_plot[:, 1].min() - 1, X_plot[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                     np.arange(y_min, y_max, h))\n",
    "\n",
    "\n",
    "# Plot the decision boundary. For that, we will assign a color to each\n",
    "# point in the mesh\n",
    "Z = clf.predict(np.array([xx.ravel(), yy.ravel()]).T)\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.contourf(xx, yy, Z,\n",
    "             alpha = 0.7, cmap = ListedColormap(('red', 'green')))\n",
    "\n",
    "\n",
    "for i, j in enumerate(np.unique(z_plot)):\n",
    "    plt.scatter(X_plot[z_plot == j, 0], X_plot[z_plot == j, 1],\n",
    "                c = ['red', 'green'][i], cmap = ListedColormap(('red', 'green')), label = j)\n",
    "   #X[:, 0], X[:, 1] \n",
    "plt.xlim(xx.min(), xx.max())\n",
    "plt.ylim(yy.min(), yy.max())\n",
    "plt.title('Gradient Boosting')\n",
    "plt.xlabel('variance')\n",
    "plt.ylabel('skewness')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Advanced\n",
    "https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_early_stopping.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}