t9q2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EE2211 Tutorial 9\n",
    "\n",
    "### by Chua Dingjuan (Oct 2022)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 2 Python Codes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) Calculate the overall MSE for the following data at depth 1 of a regression tree assuming a decision threshold is taken at x=5.0. How does it compare with the MSE at the root? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of all y values is : 2.9384615384615387\n",
      "MSE at root is :  1.2223668639053253\n",
      "Mean of y values at Depth 1 for x>5 is :  [3.85]\n",
      "MSE at Depth 1 for x>5 is : [0.59583333]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import numpy.matlib\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_squared_error as skmse\n",
    "\n",
    "#Root\n",
    "x = np.array([[1, 0.8, 2, 2.5, 3, 4, 4.2, 6, 6.3, 7, 8, 8.2, 9]]).T\n",
    "y = np.array([[2, 3,  2.5, 1,2.3,2.8,1.5, 2.6, 3.5, 4, 3.5, 5, 4.5 ]]).T\n",
    "\n",
    "linex = np.array([5, 5, 5, 5, 5, 5])\n",
    "liney = np.array([0, 1, 2, 3, 4, 5])\n",
    "plt.plot(x,y,'bx')\n",
    "#plt.plot(linex,liney,'r')\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "yave = np.mean(y)\n",
    "print (\"Mean of all y values is :\", yave)\n",
    "\n",
    "print(\"MSE at root is : \", skmse(np.matlib.repmat(yave,13,1), y))\n",
    "\n",
    "#Depth 1\n",
    "\n",
    "yave = sum(y*(x>5))/6\n",
    "print(\"Mean of y values at Depth 1 for x>5 is : \", sum(y*(x>5))/6)\n",
    "\n",
    "mse = sum((y-yave)*(y-yave)*(x>5))/6\n",
    "\n",
    "print(\"MSE at Depth 1 for x>5 is :\", mse)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}