{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using R for supervised learning\n",
    "====\n",
    "    \n",
    "This notebook goes over the basic concept of how to construct and use a supervised learning pipleine for classificaioon. We will use the k-nearest neighbors algorithm for illustration, but the baisc ideas carry over to all algorithms for classificaiton and regression. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "healthdy <- read.table('healthdy.txt', header = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>ID</th><th scope=col>GENDER</th><th scope=col>FLEXPRE</th><th scope=col>FLEXPOS</th><th scope=col>BAWPRE</th><th scope=col>BAWPOS</th><th scope=col>BWWPRE</th><th scope=col>BWWPOS</th><th scope=col>BFPPRE</th><th scope=col>BFPPOS</th><th scope=col>FVCPRE</th><th scope=col>FVPOS</th><th scope=col>METSPRE</th><th scope=col>METSPOS</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>0</td><td>1</td><td>21.000</td><td>21.500</td><td>70.5</td><td>75.6</td><td>3.3</td><td>3.7</td><td>14.58</td><td>14.17</td><td>5.1</td><td>5.1</td><td>12.7</td><td>18.0</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>2</td><td>1</td><td>21.000</td><td>21.250</td><td>71.3</td><td>70.7</td><td>3.2</td><td>3.6</td><td>16.79</td><td>13.95</td><td>4.3</td><td>4.3</td><td>11.1</td><td>12.0</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>3</td><td>1</td><td>21.500</td><td>20.000</td><td>64.5</td><td>66.6</td><td>4.1</td><td>4.0</td><td>6.6</td><td>08.98</td><td>4.5</td><td>4.5</td><td>15.3</td><td>16.7</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>4</td><td>1</td><td>23.000</td><td>23.375</td><td>97</td><td>95.0</td><td>4.4</td><td>4.3</td><td>18.04</td><td>17.32</td><td>4.7</td><td>4.3</td><td>12.0</td><td>17.5</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>5</td><td>1</td><td>21.000</td><td>21.000</td><td>71</td><td>73.2</td><td>3.7</td><td>3.8</td><td>11.12</td><td>11.50</td><td>5.8</td><td>5.8</td><td>12.2</td><td>12.2</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>6</td><td>1</td><td>20.500</td><td>20.750</td><td>72.5</td><td>73.1</td><td>3.1</td><td>3.4</td><td>17.88</td><td>16.22</td><td>4.3</td><td>4.3</td><td>11.1</td><td>10.0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllll}\n",
       "  & ID & GENDER & FLEXPRE & FLEXPOS & BAWPRE & BAWPOS & BWWPRE & BWWPOS & BFPPRE & BFPPOS & FVCPRE & FVPOS & METSPRE & METSPOS\\\\\n",
       "\\hline\n",
       "\t1 & 0 & 1 & 21.000 & 21.500 & 70.5 & 75.6 & 3.3 & 3.7 & 14.58 & 14.17 & 5.1 & 5.1 & 12.7 & 18.0\\\\\n",
       "\t2 & 2 & 1 & 21.000 & 21.250 & 71.3 & 70.7 & 3.2 & 3.6 & 16.79 & 13.95 & 4.3 & 4.3 & 11.1 & 12.0\\\\\n",
       "\t3 & 3 & 1 & 21.500 & 20.000 & 64.5 & 66.6 & 4.1 & 4.0 & 6.6 & 08.98 & 4.5 & 4.5 & 15.3 & 16.7\\\\\n",
       "\t4 & 4 & 1 & 23.000 & 23.375 & 97 & 95.0 & 4.4 & 4.3 & 18.04 & 17.32 & 4.7 & 4.3 & 12.0 & 17.5\\\\\n",
       "\t5 & 5 & 1 & 21.000 & 21.000 & 71 & 73.2 & 3.7 & 3.8 & 11.12 & 11.50 & 5.8 & 5.8 & 12.2 & 12.2\\\\\n",
       "\t6 & 6 & 1 & 20.500 & 20.750 & 72.5 & 73.1 & 3.1 & 3.4 & 17.88 & 16.22 & 4.3 & 4.3 & 11.1 & 10.0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "  ID GENDER FLEXPRE FLEXPOS BAWPRE BAWPOS BWWPRE BWWPOS BFPPRE BFPPOS FVCPRE\n",
       "1  0      1  21.000  21.500   70.5   75.6    3.3    3.7  14.58  14.17    5.1\n",
       "2  2      1  21.000  21.250   71.3   70.7    3.2    3.6  16.79  13.95    4.3\n",
       "3  3      1  21.500  20.000   64.5   66.6    4.1    4.0   6.60  08.98    4.5\n",
       "4  4      1  23.000  23.375   97.0   95.0    4.4    4.3  18.04  17.32    4.7\n",
       "5  5      1  21.000  21.000   71.0   73.2    3.7    3.8  11.12  11.50    5.8\n",
       "6  6      1  20.500  20.750   72.5   73.1    3.1    3.4  17.88  16.22    4.3\n",
       "  FVPOS METSPRE METSPOS\n",
       "1   5.1    12.7    18.0\n",
       "2   4.3    11.1    12.0\n",
       "3   4.5    15.3    16.7\n",
       "4   4.3    12.0    17.5\n",
       "5   5.8    12.2    12.2\n",
       "6   4.3    11.1    10.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "head(healthdy)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Data from Health Dynamics class at Hope College--collected about 1985 by Gregg Afman.\n",
    "Downloaded from http://www.math.hope.edu/swanson/data/healthdy.txt\n",
    "\n",
    "Gender 1 = Male\n",
    "Gender 2 = Female\n",
    "\n",
    "Flexpre = Flexability at the beginning of the semester\n",
    "Flexpro = Flexability at the end of the semester\n",
    "\n",
    "Bawpre = Air Weight at the beginning of the semester\n",
    "Bawpro = Air weight at the end of the semester\n",
    "\n",
    "Bwwpre = Water weight at the beginning of the semester\n",
    "Bwwpro = Water weight at the end of the semester\n",
    "\n",
    "Bfppre = Body fat at the beginning of the semester\n",
    "Bfppro = Body fat at the end of the semester\n",
    "\n",
    "Fvcpre = Forced capacity at the beginning of the semester\n",
    "Fvcpro = Forced capacity at the end of the semester\n",
    "\n",
    "Metspre = Mets at the beginning of the semester\n",
    "Metspro = Mets at the end of the semester"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Supervised learning problem\n",
    "\n",
    "For simplicity and ease of visualization, we will just use the first 2 indepdendent variables as fearures for predicitng gender. In practice, the selection of approprieate features to use as predictors can be a challenging problem that greatly affects the effectiveness of supervised learning.\n",
    "\n",
    "So the problme is: How accurately can we guess the gender of a student from the Flexpre and Bawpre variables? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing the data\n",
    "----\n",
    "\n",
    "First let's make a smaller dataframe containing just the variables of interest, and make some plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df <- healthdy[,c(\"ID\", \"GENDER\", \"FLEXPRE\", \"BAWPRE\")]\n",
    "df$ID <- factor(df$ID)\n",
    "df$GENDER <- factor(df$GENDER, labels = c(\"Male\", \"Female\"))\n",
    "df$FLEXPRE <- as.numeric(df$FLEXPRE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       ID         GENDER       FLEXPRE          BAWPRE     \n",
       " 0      :  2   Male  : 82   Min.   : 1.00   Min.   :35.20  \n",
       " 2      :  2   Female:100   1st Qu.:26.00   1st Qu.:57.73  \n",
       " 3      :  2                Median :42.00   Median :65.05  \n",
       " 4      :  2                Mean   :38.76   Mean   :66.99  \n",
       " 5      :  2                3rd Qu.:52.00   3rd Qu.:74.50  \n",
       " 6      :  2                Max.   :67.00   Max.   :98.50  \n",
       " (Other):170                                               "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the mean flexibilitiy and weights for boys and girls."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Gender</th><th scope=col>FLEXPRE</th><th scope=col>BAWPRE</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Male</td><td>33.46341</td><td>75.89024</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Female</td><td>43.1</td><td>59.695</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       "  & Gender & FLEXPRE & BAWPRE\\\\\n",
       "\\hline\n",
       "\t1 & Male & 33.46341 & 75.89024\\\\\n",
       "\t2 & Female & 43.1 & 59.695\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "  Gender  FLEXPRE   BAWPRE\n",
       "1   Male 33.46341 75.89024\n",
       "2 Female 43.10000 59.69500"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with(df, aggregate(df[,3:4], by=list(Gender=GENDER), FUN=mean))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On average, girls are more flexible and weigh less than boys. This is confirmed viually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      ],
      "text/plain": [
       "Plot with title “Flexibility and Weight grouped by Gender”"
      ]
     },
     "metadata": {
      "image/svg+xml": {
       "isolated": true
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(df$FLEXPRE, df$BAWPRE, col=df$GENDER,\n",
    "     xlab=\"Flexibility\", ylab=\"Weight\", \n",
    "     main=\"Flexibility and Weight grouped by Gender\")\n",
    "legend(0, 100, c(\"Male\", \"Female\"), pch=1, col=1:2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comments\n",
    "\n",
    "It looks like there is a pretty good probablility that we can guess the gender from the body weight and flexibilty alone. The k-nearest neighbor does this guessing in a very simple fashion - Given any point in the data set, it looks for the nearest k neighboring points, and simply uses the majority gender among these neighbors as the guess. In the sections below, we'll implement a supervised learnign pipeline using k-nearest neighbors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Work!\n",
    "----\n",
    "\n",
    "Review questions to make sure you are up to speed with basic data manipulation and plotting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q1**. Tabulate the median value of FLEXPRE and BAWPRE by gender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q2**. Tabluate the average change in weight from the beginnig to the end of the semester by gender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q3**. Identify from the plot above the IDs of 3 individuals for whom you expect k-nearest neighbors to make the wrong gender prediction. HInt: Make a scatterplot but add the IDs as labels for each point, using a small x-offset of 2.5 so that labels are immediately to the right of each point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting data into training and test data sets\n",
    "----\n",
    "\n",
    "We will use 3/4 of the data to train the algorithm and 1/4 to test how good it is. The reason for doing this is that if we train on the full data set, the algorithm has \"seen\" its test points before, and hence will seem more accurate than it really is with respect to new data samples. 'Holding out\" some of the data for testing that is not used for training the algorithm allows us to honestly evaluate the \"out of sample\" error accurately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "set.seed(123) # set ranodm number seed for reproducibility\n",
    "size <- floor(0.75 * nrow(df)) # desired size of training set\n",
    "df <- df[sample(nrow(df), replace = FALSE),] # shuffle rows randomly\n",
    "df.train <- df[1:size, ] # take first size rows of shuffled data frame as training set\n",
    "df.test <- df[(size+1):nrow(df), ] # take the remaining rows as the test set\n",
    "x.train <- df.train[,c(\"FLEXPRE\", \"BAWPRE\")]\n",
    "y.train <- df.train[,\"GENDER\"]\n",
    "x.test <- df.test[,c(\"FLEXPRE\", \"BAWPRE\")]\n",
    "y.test <- df.test[,\"GENDER\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       ID         GENDER      FLEXPRE          BAWPRE     \n",
       " 7      :  2   Male  :61   Min.   : 1.00   Min.   :35.20  \n",
       " 8      :  2   Female:75   1st Qu.:24.00   1st Qu.:57.70  \n",
       " 9      :  2               Median :42.00   Median :65.40  \n",
       " 10     :  2               Mean   :38.49   Mean   :67.34  \n",
       " 11     :  2               3rd Qu.:52.00   3rd Qu.:74.88  \n",
       " 12     :  2               Max.   :67.00   Max.   :98.50  \n",
       " (Other):124                                              "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary(df.train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       ID        GENDER      FLEXPRE          BAWPRE     \n",
       " 48     : 2   Male  :21   Min.   : 2.00   Min.   :41.90  \n",
       " 49     : 2   Female:25   1st Qu.:31.25   1st Qu.:58.10  \n",
       " 51     : 2               Median :42.00   Median :64.60  \n",
       " 81     : 2               Mean   :39.54   Mean   :65.97  \n",
       " 0      : 1               3rd Qu.:51.00   3rd Qu.:73.15  \n",
       " 2      : 1               Max.   :65.00   Max.   :90.20  \n",
       " (Other):36                                              "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary(df.test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train knn on training set\n",
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "library(class)\n",
    "y.pred <- knn(x.train, x.test, cl=y.train, k=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>Male</li>\n",
       "\t<li>Female</li>\n",
       "\t<li>Male</li>\n",
       "\t<li>Female</li>\n",
       "\t<li>Male</li>\n",
       "\t<li>Female</li>\n",
       "\t<li>Female</li>\n",
       "\t<li>Female</li>\n",
       "\t<li>Female</li>\n",
       "\t<li>Female</li>\n",
       "\t<li>Male</li>\n",
       "\t<li>Female</li>\n",
       "\t<li>Female</li>\n",
       "\t<li>Female</li>\n",
       "\t<li>Male</li>\n",
       "\t<li>Female</li>\n",
       "\t<li>Male</li>\n",
       "\t<li>Male</li>\n",
       "\t<li>Female</li>\n",
       "\t<li>Female</li>\n",
       "\t<li>Female</li>\n",
       "\t<li>Male</li>\n",
       "\t<li>Male</li>\n",
       "\t<li>Female</li>\n",
       "\t<li>Male</li>\n",
       "\t<li>Female</li>\n",
       "\t<li>Female</li>\n",
       "\t<li>Female</li>\n",
       "\t<li>Female</li>\n",
       "\t<li>Male</li>\n",
       "\t<li>Female</li>\n",
       "\t<li>Male</li>\n",
       "\t<li>Female</li>\n",
       "\t<li>Male</li>\n",
       "\t<li>Female</li>\n",
       "\t<li>Male</li>\n",
       "\t<li>Female</li>\n",
       "\t<li>Female</li>\n",
       "\t<li>Male</li>\n",
       "\t<li>Male</li>\n",
       "\t<li>Female</li>\n",
       "\t<li>Female</li>\n",
       "\t<li>Male</li>\n",
       "\t<li>Male</li>\n",
       "\t<li>Male</li>\n",
       "\t<li>Male</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item Male\n",
       "\\item Female\n",
       "\\item Male\n",
       "\\item Female\n",
       "\\item Male\n",
       "\\item Female\n",
       "\\item Female\n",
       "\\item Female\n",
       "\\item Female\n",
       "\\item Female\n",
       "\\item Male\n",
       "\\item Female\n",
       "\\item Female\n",
       "\\item Female\n",
       "\\item Male\n",
       "\\item Female\n",
       "\\item Male\n",
       "\\item Male\n",
       "\\item Female\n",
       "\\item Female\n",
       "\\item Female\n",
       "\\item Male\n",
       "\\item Male\n",
       "\\item Female\n",
       "\\item Male\n",
       "\\item Female\n",
       "\\item Female\n",
       "\\item Female\n",
       "\\item Female\n",
       "\\item Male\n",
       "\\item Female\n",
       "\\item Male\n",
       "\\item Female\n",
       "\\item Male\n",
       "\\item Female\n",
       "\\item Male\n",
       "\\item Female\n",
       "\\item Female\n",
       "\\item Male\n",
       "\\item Male\n",
       "\\item Female\n",
       "\\item Female\n",
       "\\item Male\n",
       "\\item Male\n",
       "\\item Male\n",
       "\\item Male\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. Male\n",
       "2. Female\n",
       "3. Male\n",
       "4. Female\n",
       "5. Male\n",
       "6. Female\n",
       "7. Female\n",
       "8. Female\n",
       "9. Female\n",
       "10. Female\n",
       "11. Male\n",
       "12. Female\n",
       "13. Female\n",
       "14. Female\n",
       "15. Male\n",
       "16. Female\n",
       "17. Male\n",
       "18. Male\n",
       "19. Female\n",
       "20. Female\n",
       "21. Female\n",
       "22. Male\n",
       "23. Male\n",
       "24. Female\n",
       "25. Male\n",
       "26. Female\n",
       "27. Female\n",
       "28. Female\n",
       "29. Female\n",
       "30. Male\n",
       "31. Female\n",
       "32. Male\n",
       "33. Female\n",
       "34. Male\n",
       "35. Female\n",
       "36. Male\n",
       "37. Female\n",
       "38. Female\n",
       "39. Male\n",
       "40. Male\n",
       "41. Female\n",
       "42. Female\n",
       "43. Male\n",
       "44. Male\n",
       "45. Male\n",
       "46. Male\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] Male   Female Male   Female Male   Female Female Female Female Female\n",
       "[11] Male   Female Female Female Male   Female Male   Male   Female Female\n",
       "[21] Female Male   Male   Female Male   Female Female Female Female Male  \n",
       "[31] Female Male   Female Male   Female Male   Female Female Male   Male  \n",
       "[41] Female Female Male   Male   Male   Male  \n",
       "Levels: Male Female"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate the model\n",
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "        y.test\n",
       "y.pred   Male Female\n",
       "  Male     16      4\n",
       "  Female    5     21"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table(y.pred, y.test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Who was predicted wrongly?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "misses <- y.pred != y.test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>ID</th><th scope=col>GENDER</th><th scope=col>FLEXPRE</th><th scope=col>BAWPRE</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>56</th><td>56</td><td>Male</td><td>26</td><td>63.7</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>3</td><td>Male</td><td>46</td><td>64.5</td></tr>\n",
       "\t<tr><th scope=row>160</th><td>77</td><td>Female</td><td>24</td><td>73.3</td></tr>\n",
       "\t<tr><th scope=row>73</th><td>73</td><td>Male</td><td>53</td><td>64.7</td></tr>\n",
       "\t<tr><th scope=row>15</th><td>15</td><td>Male</td><td>44</td><td>72.7</td></tr>\n",
       "\t<tr><th scope=row>51</th><td>51</td><td>Male</td><td>56</td><td>61.5</td></tr>\n",
       "\t<tr><th scope=row>120</th><td>37</td><td>Female</td><td>56</td><td>69.9</td></tr>\n",
       "\t<tr><th scope=row>100</th><td>17</td><td>Female</td><td>42</td><td>67.3</td></tr>\n",
       "\t<tr><th scope=row>99</th><td>16</td><td>Female</td><td>7</td><td>64.8</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & ID & GENDER & FLEXPRE & BAWPRE\\\\\n",
       "\\hline\n",
       "\t56 & 56 & Male & 26 & 63.7\\\\\n",
       "\t3 & 3 & Male & 46 & 64.5\\\\\n",
       "\t160 & 77 & Female & 24 & 73.3\\\\\n",
       "\t73 & 73 & Male & 53 & 64.7\\\\\n",
       "\t15 & 15 & Male & 44 & 72.7\\\\\n",
       "\t51 & 51 & Male & 56 & 61.5\\\\\n",
       "\t120 & 37 & Female & 56 & 69.9\\\\\n",
       "\t100 & 17 & Female & 42 & 67.3\\\\\n",
       "\t99 & 16 & Female & 7 & 64.8\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "    ID GENDER FLEXPRE BAWPRE\n",
       "56  56   Male      26   63.7\n",
       "3    3   Male      46   64.5\n",
       "160 77 Female      24   73.3\n",
       "73  73   Male      53   64.7\n",
       "15  15   Male      44   72.7\n",
       "51  51   Male      56   61.5\n",
       "120 37 Female      56   69.9\n",
       "100 17 Female      42   67.3\n",
       "99  16 Female       7   64.8"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.test[misses,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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       "</svg>\n"
      ],
      "text/plain": [
       "Plot with title “Flexibility and Weight grouped by Gender”"
      ]
     },
     "metadata": {
      "image/svg+xml": {
       "isolated": true
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(df.test$FLEXPRE, df.test$BAWPRE, col=df.test$GENDER,\n",
    "     xlab=\"Flexibility\", ylab=\"Weight\", \n",
    "     main=\"Flexibility and Weight grouped by Gender\")\n",
    "points(df.test$FLEXPRE[misses], df.test$BAWPRE[misses], col=\"blue\", cex=2)\n",
    "\n",
    "legend(0, 100, c(\"Male\", \"Female\"), pch=1, col=1:2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Work!\n",
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Repeat the analysis using the Bfppre and Fvcpre variables as predictors instead. Do you get better or worse predictions?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q!**. Extract the relevant variables into a new data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q2**. Visualize Bfppre and Fvcpre grouped by gender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q3**. Split the data into training and test data sets using a 2/3, 1/3 ratio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q4**. Find the predictions make by knn with 5 neighbors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q5**.. Make a table of true positives, false positives, true negatives and false negatives. Calculate\n",
    "\n",
    "1. accuracy\n",
    "2. sensitivity\n",
    "3. specificity\n",
    "4. possitive predicrvie value\n",
    "5. negative predictive value\n",
    "6. f-score (harmonic mean of senistivity and specificity)\n",
    "\n",
    "Look up definitions in Wikipedia if you don't know what these mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q6** Make a plot to identify mis-classified subjects if any."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cross-validation\n",
    "----\n",
    "\n",
    "Splitting into training and test data set is all well and good, but when the amound data we have is small, it is wastful to have 1/4 or 1/3 as \"hold out\" test data that is not used to train the algorithm. An alternaitve is to perform cross-validation, in which we split the data into k equal groups and cycle through all possible combinatinos of k-1 training and 1 test group. For example, if we split the data into 4 groups (\"4-fold cross-validation\"), we would do\n",
    "\n",
    "- Train on 1,2,3 and Test on 4\n",
    "- Train on 1,2,4 and Test on 3\n",
    "- Train on 1,3,4 and Test on 2\n",
    "- Train on 2,3,4 and Test on 1\n",
    "\n",
    "then finally sum the test results to evalate the algorithm's performance. The limiting example where we split into as many n groups (where n is the number of data poits) and test on only 1 data point  each time is known as Leave-One-Out-Cross-Validation (LOOCV)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LOOCV\n",
    "\n",
    "We will use a simple (inefficient) loop version of the algorithm that should be quite easy to understand."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we will recreat the data set in case you overwrote the variables in the exercises."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df <- healthdy[,c(\"ID\", \"GENDER\", \"FLEXPRE\", \"BAWPRE\")]\n",
    "df$ID <- factor(df$ID)\n",
    "df$GENDER <- factor(df$GENDER, labels = c(\"Male\", \"Female\"))\n",
    "df$FLEXPRE <- as.numeric(df$FLEXPRE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       ID         GENDER       FLEXPRE          BAWPRE     \n",
       " 0      :  2   Male  : 82   Min.   : 1.00   Min.   :35.20  \n",
       " 2      :  2   Female:100   1st Qu.:26.00   1st Qu.:57.73  \n",
       " 3      :  2                Median :42.00   Median :65.05  \n",
       " 4      :  2                Mean   :38.76   Mean   :66.99  \n",
       " 5      :  2                3rd Qu.:52.00   3rd Qu.:74.50  \n",
       " 6      :  2                Max.   :67.00   Max.   :98.50  \n",
       " (Other):170                                               "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y.test <- df[,\"GENDER\"]\n",
    "y.pred <- y.test # we will overwirite the entries in the loop\n",
    "for (i in 1:nrow(df)) {\n",
    "    x.test <- df[i, c(\"FLEXPRE\", \"BAWPRE\")]\n",
    "    x.train <- df[-i, c(\"FLEXPRE\", \"BAWPRE\")] # the minus menas keep all rows except i\n",
    "    y.train <- df[-i, \"GENDER\"]\n",
    "    y.pred[i] <- knn(x.train, x.test, cl=y.train, k=3)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "        y.test\n",
       "y.pred   Male Female\n",
       "  Male     62     18\n",
       "  Female   20     82"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table(y.pred, y.test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      ],
      "text/plain": [
       "Plot with title “Flexibility and Weight grouped by Gender”"
      ]
     },
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      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "misses <- y.test != y.pred\n",
    "\n",
    "plot(df$FLEXPRE, df$BAWPRE, col=df$GENDER,\n",
    "     xlab=\"Flexibility\", ylab=\"Weight\", \n",
    "     main=\"Flexibility and Weight grouped by Gender\")\n",
    "points(df$FLEXPRE[misses], df$BAWPRE[misses], col=\"blue\", cex=2)\n",
    "\n",
    "legend(0, 100, c(\"Male\", \"Female\"), pch=1, col=1:2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Work!\n",
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q1**. Increase the number of neighbors to 7. Does it improve the reuslts? Whatt are the tradeoff of increasing or decraesaing the number of neighbors?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q2**. Implement 5-fold cross-validation for the FLEXPRE and BAWPRE variables. Tablutate the hits and misses and make a plot as in the previous examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  }
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