Madrid Java User Group (Madrid JUG) Madrid Java User Group (Madrid JUG)
This is a classification problem (machine learning)
Dataset ZipCode (Dimensions) http://www-stat.stanford.edu/~tibs/ElemStatLearn/datasets/zip.info
Normalized handwritten digits, automatically scanned from envelopes by the U.S. Postal Service. The original scanned digits are binary and of different sizes and orientations; the images here have been deslanted and size normalized, resulting in 16 x 16 grayscale images (Le Cun et al., 1990).
The data are in two gzipped files, and each line consists of the digit id (0-9) followed by the 256 grayscale values.
001 002 003 004 … 015 016
017 018 019 020 … 031 032
033 034 035 036 … 037 038
………………………
209 210 211 212 … 223 224
225 226 227 228 … 239 240
241 242 243 244 … 255 256
getwd()
## [1] "/home/gsantos/R/RStats/MadridJUG-DataMining"
WORKING_DIR <- "~/R/RStats/MadridJUG-DataMining/"
# WORKING_DIR <- 'C:/Users/gsantos/R/RStats/MadridJUG-DataMining'
DATASET_DIR <- "./data/"
FIGURES_DIR <- "./figures/"
COLORS <- c("white", "black")
setwd(WORKING_DIR)
getwd()
## [1] "/home/gsantos/R/RStats/MadridJUG-DataMining"
### Load Libraries
### install.packages(c('knitr','RColorBrewer','ElemStatLearn','foreign','tree','RWeka','rpart','maptree','e1071'))
install.packages(c("RColorBrewer", "ElemStatLearn", "foreign", "tree", "RWeka",
"rpart", "maptree", "e1071"))
## Installing packages into '/home/gsantos/R/i686-pc-linux-gnu-library/3.0'
## (as 'lib' is unspecified)
## Error: trying to use CRAN without setting a mirror
library(knitr) # Markdown
library(RColorBrewer)
library(ElemStatLearn)
library(foreign) # For reading and writing data stored by statistical packages such as Minitab,S,SAS,SPSS
library(tree)
library(maptree)
## Loading required package: cluster
## Loading required package: rpart
library(rpart) # Recursive Partitioning and Regression Trees (RPart)
library(RWeka) # Weka
## Attaching package: 'RWeka'
## The following object is masked from 'package:foreign':
##
## read.arff, write.arff
library(FNN) # Fast k-Nearest Neighbors (kNN)
library(e1071) # Support Vector Machine (SVM)
## Loading required package: class
## Attaching package: 'class'
## The following object is masked from 'package:FNN':
##
## knn, knn.cv
### Set Color colorRampPalette(COLORS) ( 4 ) ## (n)
CUSTOM_COLORS <- colorRampPalette(colors = COLORS)
CUSTOM_COLORS_PLOT <- colorRampPalette(brewer.pal(10, "Set3"))
# Figures Label
opts_chunk$set(fig.path = "figures/plot-handwritten")
# opts_chunk$set(echo=FALSE, fig.path='figures/plot-hw-', cache=TRUE)
### Load data
DATASET.train <- as.data.frame(zip.train)
DATASET.test <- as.data.frame(zip.test)
head(DATASET.train)
## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12
## 1 6 -1 -1 -1 -1.000 -1.000 -1.000 -1.000 -0.631 0.862 -0.167 -1.000
## 2 5 -1 -1 -1 -0.813 -0.671 -0.809 -0.887 -0.671 -0.853 -1.000 -1.000
## 3 4 -1 -1 -1 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.996 0.147
## 4 7 -1 -1 -1 -1.000 -1.000 -0.273 0.684 0.960 0.450 -0.067 -0.679
## 5 3 -1 -1 -1 -1.000 -1.000 -0.928 -0.204 0.751 0.466 0.234 -0.809
## 6 6 -1 -1 -1 -1.000 -1.000 -0.397 0.983 -0.535 -1.000 -1.000 -1.000
## V13 V14 V15 V16 V17 V18 V19 V20 V21 V22 V23 V24
## 1 -1.000 -1.000 -1.000 -1.000 -1 -1 -1 -1 -1.000 -1.000 -1.000 -0.992
## 2 -0.774 -0.180 0.052 -0.241 -1 -1 -1 -1 0.392 1.000 0.857 0.727
## 3 1.000 -0.189 -1.000 -1.000 -1 -1 -1 -1 -1.000 -1.000 -1.000 -1.000
## 4 -1.000 -1.000 -1.000 -1.000 -1 -1 -1 -1 -1.000 -0.114 0.974 0.917
## 5 -1.000 -1.000 -1.000 -1.000 -1 -1 -1 -1 -1.000 -0.370 0.739 1.000
## 6 -1.000 -1.000 -1.000 -1.000 -1 -1 -1 -1 -1.000 -1.000 0.692 0.536
## V25 V26 V27 V28 V29 V30 V31 V32 V33 V34 V35 V36
## 1 0.297 1.000 0.307 -1.000 -1.000 -1.000 -1.000 -1.000 -1 -1 -1 -1
## 2 1.000 0.805 0.613 0.613 0.860 1.000 1.000 0.396 -1 -1 -1 -1
## 3 -1.000 -1.000 -0.882 1.000 0.390 -0.811 -1.000 -1.000 -1 -1 -1 -1
## 4 0.734 0.994 1.000 0.973 0.391 -0.421 -0.976 -1.000 -1 -1 -1 -1
## 5 1.000 1.000 1.000 0.644 -0.890 -1.000 -1.000 -1.000 -1 -1 -1 -1
## 6 -0.767 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1 -1 -1 -1
## V37 V38 V39 V40 V41 V42 V43 V44 V45 V46
## 1 -1.000 -1.000 -1.000 -0.410 1.000 0.986 -0.565 -1.000 -1.000 -1
## 2 -0.548 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1
## 3 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.715 1.000 0.029 -1
## 4 -0.323 0.991 0.622 -0.738 -1.000 -0.639 0.023 0.871 1.000 1
## 5 -1.000 0.616 1.000 0.688 -0.455 -0.731 0.659 1.000 -0.287 -1
## 6 -1.000 -0.921 0.928 -0.118 -1.000 -1.000 -1.000 -1.000 -1.000 -1
## V47 V48 V49 V50 V51 V52 V53 V54 V55 V56 V57 V58
## 1 -1.000 -1.000 -1.000 -1 -1 -1 -1.000 -1.000 -0.683 0.825 1 0.562
## 2 1.000 0.875 -0.957 -1 -1 -1 -0.786 0.961 1.000 1.000 1 0.727
## 3 -1.000 -1.000 -1.000 -1 -1 -1 -1.000 -0.888 -0.912 -1.000 -1 -1.000
## 4 -0.432 -1.000 -1.000 -1 -1 -1 0.409 1.000 0.000 -1.000 -1 -1.000
## 5 -1.000 -1.000 -1.000 -1 -1 -1 -1.000 -0.376 -0.186 -0.874 -1 -1.000
## 6 -1.000 -1.000 -1.000 -1 -1 -1 -1.000 -0.394 1.000 -0.596 -1 -1.000
## V59 V60 V61 V62 V63 V64 V65 V66 V67 V68 V69
## 1 -1.000 -1.000 -1.000 -1.000 -1.000 -1.00 -1 -1 -1 -1.000 -1.000
## 2 0.403 0.403 0.171 -0.314 -0.314 -0.94 -1 -1 -1 -1.000 -0.298
## 3 -0.549 1.000 0.361 -1.000 -1.000 -1.00 -1 -1 -1 -1.000 -0.938
## 4 -1.000 -0.842 0.714 1.000 -0.534 -1.00 -1 -1 -1 -0.879 0.965
## 5 -0.014 1.000 -0.253 -1.000 -1.000 -1.00 -1 -1 -1 -1.000 -1.000
## 6 -1.000 -1.000 -1.000 -1.000 -1.000 -1.00 -1 -1 -1 -1.000 -1.000
## V70 V71 V72 V73 V74 V75 V76 V77 V78 V79
## 1 -0.938 0.540 1.000 0.778 -0.715 -1.000 -1.000 -1.000 -1.000 -1.000
## 2 1.000 1.000 1.000 0.440 0.056 -0.755 -1.000 -1.000 -1.000 -1.000
## 3 0.694 0.057 -1.000 -1.000 -1.000 -0.382 1.000 0.511 -1.000 -1.000
## 4 1.000 -0.713 -1.000 -1.000 -1.000 -1.000 -0.606 0.977 0.695 -0.906
## 5 -1.000 -1.000 -1.000 -1.000 -0.978 0.501 1.000 -0.540 -1.000 -1.000
## 6 0.060 0.900 -0.951 -1.000 -1.000 -1.000 -0.647 0.455 -0.333 -1.000
## V80 V81 V82 V83 V84 V85 V86 V87 V88 V89 V90 V91
## 1 -1 -1 -1 -1 -1.000 -1.000 0.100 1.000 0.922 -0.439 -1.000 -1.000
## 2 -1 -1 -1 -1 -1.000 0.366 1.000 1.000 1.000 1.000 1.000 0.889
## 3 -1 -1 -1 -1 -1.000 -0.311 1.000 -0.043 -1.000 -1.000 -1.000 -0.648
## 4 -1 -1 -1 -1 -0.528 1.000 0.931 -0.888 -1.000 -1.000 -1.000 -0.949
## 5 -1 -1 -1 -1 -1.000 -1.000 -1.000 -0.998 -0.341 0.296 0.371 1.000
## 6 -1 -1 -1 -1 -1.000 -1.000 0.259 0.676 -1.000 -1.000 -1.000 -0.984
## V92 V93 V94 V95 V96 V97 V98 V99 V100 V101 V102 V103
## 1 -1.000 -1.000 -1.000 -1 -1 -1 -1 -1 -1.00 -0.257 0.950 1.000
## 2 -0.081 -0.920 -1.000 -1 -1 -1 -1 -1 -1.00 -0.396 0.886 0.974
## 3 1.000 0.644 -1.000 -1 -1 -1 -1 -1 -1.00 0.489 1.000 -0.493
## 4 0.559 0.984 -0.363 -1 -1 -1 -1 -1 -0.97 -0.266 -0.555 -1.000
## 5 0.417 -0.989 -1.000 -1 -1 -1 -1 -1 -1.00 -1.000 -1.000 -0.008
## 6 0.677 0.981 0.551 -1 -1 -1 -1 -1 -1.00 -0.994 0.699 0.305
## V104 V105 V106 V107 V108 V109 V110 V111 V112 V113 V114 V115
## 1 -0.162 -1.000 -1.00 -1.000 -0.987 -0.714 -0.832 -1 -1 -1 -1 -1
## 2 0.851 0.851 0.95 1.000 1.000 0.539 -0.754 -1 -1 -1 -1 -1
## 3 -1.000 -1.000 -1.00 -0.564 1.000 0.693 -1.000 -1 -1 -1 -1 -1
## 4 -1.000 -1.000 -1.00 -0.186 1.000 0.488 -1.000 -1 -1 -1 -1 -1
## 5 1.000 1.000 1.00 1.000 0.761 -0.731 -1.000 -1 -1 -1 -1 -1
## 6 -1.000 -1.000 -1.00 -0.499 1.000 -0.092 0.751 -1 -1 -1 -1 -1
## V116 V117 V118 V119 V120 V121 V122 V123 V124 V125
## 1 -0.797 0.909 1.000 0.300 -0.961 -1 -1.000 -0.550 0.485 0.996
## 2 -1.000 -1.000 -0.886 -0.505 -1.000 -1 -0.649 0.405 1.000 1.000
## 3 -0.966 0.988 1.000 -0.893 -1.000 -1 -1.000 -0.397 1.000 0.903
## 4 -1.000 -1.000 -1.000 -1.000 -1.000 -1 -1.000 0.697 0.992 -0.458
## 5 -1.000 -1.000 -1.000 0.242 1.000 1 0.319 0.259 1.000 0.742
## 6 -1.000 -0.923 0.966 -0.107 -1.000 -1 -1.000 -0.300 0.854 -0.382
## V126 V127 V128 V129 V130 V131 V132 V133 V134 V135 V136
## 1 0.867 0.092 -1 -1 -1 -1 0.278 1.000 0.877 -0.824 -1.000
## 2 0.653 -0.838 -1 -1 -1 -1 -1.000 -1.000 -1.000 -1.000 -1.000
## 3 -0.977 -1.000 -1 -1 -1 -1 -0.559 1.000 1.000 -0.297 -1.000
## 4 -1.000 -1.000 -1 -1 -1 -1 -1.000 -1.000 -1.000 -1.000 -1.000
## 5 -0.757 -1.000 -1 -1 -1 -1 -1.000 -1.000 -1.000 -0.975 -0.467
## 6 0.617 -1.000 -1 -1 -1 -1 -1.000 -0.409 1.000 -0.529 -1.000
## V137 V138 V139 V140 V141 V142 V143 V144 V145 V146
## 1 -0.905 0.145 0.977 1.000 1.000 1.000 0.990 -0.745 -1 -1.00
## 2 -1.000 -1.000 -1.000 -0.550 0.993 1.000 0.618 -0.869 -1 -0.96
## 3 -1.000 -1.000 -0.611 1.000 0.873 -0.698 -0.552 -1.000 -1 -1.00
## 4 -1.000 -0.341 1.000 0.608 -1.000 -1.000 -1.000 -1.000 -1 -1.00
## 5 -0.989 -1.000 -1.000 -0.171 0.998 0.669 -0.945 -1.000 -1 -1.00
## 6 -1.000 -1.000 0.048 0.614 -0.268 0.544 -1.000 -1.000 -1 -1.00
## V147 V148 V149 V150 V151 V152 V153 V154 V155 V156
## 1 -0.950 0.847 1.000 0.327 -1.000 -1.000 0.355 1.000 0.655 -0.109
## 2 -0.512 0.134 -0.343 -0.796 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
## 3 -1.000 -0.126 1.000 1.000 0.766 -0.764 -1.000 -1.000 -0.577 1.000
## 4 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 0.471 0.998 -0.416
## 5 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
## 6 -1.000 -1.000 0.050 0.971 -0.839 -1.000 -1.000 -1.000 0.172 0.526
## V157 V158 V159 V160 V161 V162 V163 V164 V165 V166
## 1 -0.185 1.000 0.988 -0.723 -1 -1.000 -0.63 1.000 1.000 0.068
## 2 -0.432 0.994 1.000 0.223 -1 0.426 1.00 1.000 1.000 0.214
## 3 0.933 0.484 -0.197 -1.000 -1 -1.000 -1.00 -0.818 -0.355 0.334
## 4 -1.000 -1.000 -1.000 -1.000 -1 -1.000 -1.00 -1.000 -1.000 -1.000
## 5 0.228 1.000 0.038 -1.000 -1 -1.000 -1.00 -1.000 -1.000 -1.000
## 6 -0.003 0.307 -1.000 -1.000 -1 -1.000 -1.00 -1.000 0.398 0.459
## V167 V168 V169 V170 V171 V172 V173 V174 V175 V176
## 1 -0.925 0.113 0.960 0.308 -0.884 -1.000 -0.075 1.000 0.641 -0.995
## 2 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 0.292 1.000 0.967
## 3 1.000 0.868 -0.289 -0.677 -0.596 1.000 1.000 1.000 -0.581 -1.000
## 4 -1.000 -1.000 -0.644 0.963 0.590 -0.999 -1.000 -1.000 -1.000 -1.000
## 5 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.826 0.918 0.933 -0.794
## 6 -1.000 -1.000 -1.000 -1.000 0.372 0.555 0.520 -0.045 -1.000 -1.000
## V177 V178 V179 V180 V181 V182 V183 V184 V185 V186
## 1 -1.00 -1.000 -0.677 1.000 1.000 0.753 0.341 1 0.707 -0.942
## 2 -0.88 0.449 1.000 0.896 -0.094 -0.750 -1.000 -1 -1.000 -1.000
## 3 -1.00 -1.000 -1.000 -1.000 -1.000 -0.954 0.118 1 1.000 1.000
## 4 -1.00 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1 0.061 1.000
## 5 -1.00 -1.000 -1.000 -0.666 0.337 0.224 -0.908 -1 -1.000 -1.000
## 6 -1.00 -1.000 -1.000 -1.000 0.671 0.176 -1.000 -1 -1.000 -1.000
## V187 V188 V189 V190 V191 V192 V193 V194 V195 V196
## 1 -1.000 -1.000 0.545 1.000 0.027 -1.000 -1.000 -1.000 -0.903 0.792
## 2 -1.000 -1.000 -1.000 -0.627 1.000 1.000 0.198 -0.105 1.000 1.000
## 3 1.000 1.000 0.973 -0.092 -0.995 -1.000 -1.000 -1.000 -1.000 -1.000
## 4 -0.079 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
## 5 -1.000 -1.000 -1.000 0.418 1.000 -0.258 -1.000 -1.000 -0.246 1.000
## 6 0.236 0.934 0.971 -0.712 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
## V197 V198 V199 V200 V201 V202 V203 V204 V205 V206
## 1 1.000 1.000 1.000 1.000 0.536 0.184 0.812 0.837 0.978 0.864
## 2 1.000 0.639 -0.168 -0.314 -0.446 -1.000 -1.000 -0.999 -0.337 0.147
## 3 -1.000 -1.000 -0.993 -0.464 0.046 0.290 0.457 1.000 0.721 -1.000
## 4 -1.000 -1.000 -1.000 -1.000 0.773 0.958 -0.714 -1.000 -1.000 -1.000
## 5 1.000 0.355 -0.958 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.077
## 6 0.763 0.084 -1.000 -1.000 -1.000 -1.000 0.073 1.000 0.265 -1.000
## V207 V208 V209 V210 V211 V212 V213 V214 V215 V216
## 1 -0.630 -1.000 -1.000 -1.000 -1.000 -0.452 0.828 1.000 1.000 1.000
## 2 0.996 1.000 0.667 -0.808 0.065 0.993 1.000 1.000 1.000 1.000
## 3 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
## 4 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.545
## 5 1.000 0.344 -1.000 -1.000 0.075 1.000 1.000 0.649 0.256 -0.200
## 6 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 0.563 0.210 -1.000 -1.000
## V217 V218 V219 V220 V221 V222 V223 V224 V225 V226 V227
## 1 1.000 1.000 1.000 1.000 1.000 0.135 -1 -1.000 -1.000 -1 -1.000
## 2 0.996 0.970 0.970 0.970 0.998 1.000 1 1.000 0.109 -1 -1.000
## 3 -1.000 -1.000 -0.426 1.000 0.555 -1.000 -1 -1.000 -1.000 -1 -1.000
## 4 0.989 0.432 -1.000 -1.000 -1.000 -1.000 -1 -1.000 -1.000 -1 -1.000
## 5 -0.351 -0.733 -0.733 -0.733 -0.433 0.649 1 0.093 -1.000 -1 -0.959
## 6 -0.930 -0.127 0.890 0.935 -0.845 -1.000 -1 -1.000 -1.000 -1 -1.000
## V228 V229 V230 V231 V232 V233 V234 V235 V236 V237
## 1 -1.000 -0.483 0.813 1.000 1.000 1.000 1.000 1.000 1.000 0.219
## 2 -0.830 -0.242 0.350 0.800 1.000 1.000 1.000 1.000 1.000 1.000
## 3 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 0.024 1.000 0.388
## 4 -1.000 -1.000 -1.000 -1.000 -0.348 1.000 0.798 -0.935 -1.000 -1.000
## 5 -0.062 0.821 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
## 6 -1.000 0.093 0.793 -0.205 0.214 0.746 0.918 0.692 0.954 -0.882
## V238 V239 V240 V241 V242 V243 V244 V245 V246 V247 V248
## 1 -0.943 -1.000 -1.000 -1.00 -1 -1 -1 -1.000 -0.974 -0.429 0.304
## 2 1.000 1.000 0.616 -0.93 -1 -1 -1 -1.000 -1.000 -0.858 -0.671
## 3 -1.000 -1.000 -1.000 -1.00 -1 -1 -1 -1.000 -1.000 -1.000 -1.000
## 4 -1.000 -1.000 -1.000 -1.00 -1 -1 -1 -1.000 -1.000 -1.000 -0.318
## 5 1.000 0.583 -0.843 -1.00 -1 -1 -1 -0.877 -0.326 0.174 0.466
## 6 -1.000 -1.000 -1.000 -1.00 -1 -1 -1 -0.898 0.323 1.000 0.803
## V249 V250 V251 V252 V253 V254 V255 V256 V257
## 1 0.823 1.000 0.482 -0.474 -0.991 -1.000 -1.000 -1.000 -1
## 2 -0.671 -0.033 0.761 0.762 0.126 -0.095 -0.671 -0.828 -1
## 3 -1.000 -1.000 -0.109 1.000 -0.179 -1.000 -1.000 -1.000 -1
## 4 1.000 0.536 -0.987 -1.000 -1.000 -1.000 -1.000 -1.000 -1
## 5 0.639 1.000 1.000 0.791 0.439 -0.199 -0.883 -1.000 -1
## 6 0.015 -0.862 -0.871 -0.437 -1.000 -1.000 -1.000 -1.000 -1
dim(DATASET.train)
## [1] 7291 257
nrow(DATASET.train)
## [1] 7291
ncol(DATASET.train)
## [1] 257
colnames(DATASET.train)
## [1] "V1" "V2" "V3" "V4" "V5" "V6" "V7" "V8" "V9" "V10"
## [11] "V11" "V12" "V13" "V14" "V15" "V16" "V17" "V18" "V19" "V20"
## [21] "V21" "V22" "V23" "V24" "V25" "V26" "V27" "V28" "V29" "V30"
## [31] "V31" "V32" "V33" "V34" "V35" "V36" "V37" "V38" "V39" "V40"
## [41] "V41" "V42" "V43" "V44" "V45" "V46" "V47" "V48" "V49" "V50"
## [51] "V51" "V52" "V53" "V54" "V55" "V56" "V57" "V58" "V59" "V60"
## [61] "V61" "V62" "V63" "V64" "V65" "V66" "V67" "V68" "V69" "V70"
## [71] "V71" "V72" "V73" "V74" "V75" "V76" "V77" "V78" "V79" "V80"
## [81] "V81" "V82" "V83" "V84" "V85" "V86" "V87" "V88" "V89" "V90"
## [91] "V91" "V92" "V93" "V94" "V95" "V96" "V97" "V98" "V99" "V100"
## [101] "V101" "V102" "V103" "V104" "V105" "V106" "V107" "V108" "V109" "V110"
## [111] "V111" "V112" "V113" "V114" "V115" "V116" "V117" "V118" "V119" "V120"
## [121] "V121" "V122" "V123" "V124" "V125" "V126" "V127" "V128" "V129" "V130"
## [131] "V131" "V132" "V133" "V134" "V135" "V136" "V137" "V138" "V139" "V140"
## [141] "V141" "V142" "V143" "V144" "V145" "V146" "V147" "V148" "V149" "V150"
## [151] "V151" "V152" "V153" "V154" "V155" "V156" "V157" "V158" "V159" "V160"
## [161] "V161" "V162" "V163" "V164" "V165" "V166" "V167" "V168" "V169" "V170"
## [171] "V171" "V172" "V173" "V174" "V175" "V176" "V177" "V178" "V179" "V180"
## [181] "V181" "V182" "V183" "V184" "V185" "V186" "V187" "V188" "V189" "V190"
## [191] "V191" "V192" "V193" "V194" "V195" "V196" "V197" "V198" "V199" "V200"
## [201] "V201" "V202" "V203" "V204" "V205" "V206" "V207" "V208" "V209" "V210"
## [211] "V211" "V212" "V213" "V214" "V215" "V216" "V217" "V218" "V219" "V220"
## [221] "V221" "V222" "V223" "V224" "V225" "V226" "V227" "V228" "V229" "V230"
## [231] "V231" "V232" "V233" "V234" "V235" "V236" "V237" "V238" "V239" "V240"
## [241] "V241" "V242" "V243" "V244" "V245" "V246" "V247" "V248" "V249" "V250"
## [251] "V251" "V252" "V253" "V254" "V255" "V256" "V257"
summary(DATASET.train)
## V1 V2 V3 V4
## Min. :0.0 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:1.0 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :4.0 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :3.9 Mean :-0.996 Mean :-0.981 Mean :-0.951
## 3rd Qu.:7.0 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000
## Max. :9.0 Max. : 0.638 Max. : 1.000 Max. : 1.000
## V5 V6 V7 V8
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-0.719
## Mean :-0.888 Mean :-0.773 Mean :-0.610 Mean :-0.369
## 3rd Qu.:-1.000 3rd Qu.:-0.962 3rd Qu.:-0.391 3rd Qu.: 0.255
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V9 V10 V11 V12
## Min. :-1.0000 Min. :-1.0000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-0.9990 1st Qu.:-0.9500 1st Qu.:-1.000 1st Qu.:-1.000
## Median : 0.0610 Median : 0.0020 Median :-0.561 Median :-0.995
## Mean :-0.0458 Mean :-0.0526 Mean :-0.285 Mean :-0.504
## 3rd Qu.: 0.6960 3rd Qu.: 0.6745 3rd Qu.: 0.438 3rd Qu.:-0.012
## Max. : 1.0000 Max. : 1.0000 Max. : 1.000 Max. : 1.000
## V13 V14 V15 V16
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.686 Mean :-0.815 Mean :-0.906 Mean :-0.966
## 3rd Qu.:-0.685 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V17 V18 V19 V20
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.993 Mean :-0.990 Mean :-0.951 Mean :-0.865
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000
## Max. : 0.752 Max. : 0.776 Max. : 1.000 Max. : 1.000
## V21 V22 V23 V24
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-0.904
## Median :-1.000 Median :-1.000 Median :-0.429 Median : 0.387
## Mean :-0.696 Mean :-0.434 Mean :-0.143 Mean : 0.131
## 3rd Qu.:-0.881 3rd Qu.: 0.254 3rd Qu.: 0.876 3rd Qu.: 1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V25 V26 V27 V28
## Min. :-1.000 Min. :-1.0000 Min. :-1.000 Min. :-1.0000
## 1st Qu.:-0.101 1st Qu.:-0.0535 1st Qu.:-0.849 1st Qu.:-1.0000
## Median : 0.855 Median : 0.8210 Median : 0.410 Median :-0.1570
## Mean : 0.412 Mean : 0.4148 Mean : 0.149 Mean :-0.0743
## 3rd Qu.: 1.000 3rd Qu.: 1.0000 3rd Qu.: 1.000 3rd Qu.: 0.9165
## Max. : 1.000 Max. : 1.0000 Max. : 1.000 Max. : 1.0000
## V29 V30 V31 V32
## Min. :-1.000 Min. :-1.000 Min. :-1.0 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.0 1st Qu.:-1.000
## Median :-0.952 Median :-1.000 Median :-1.0 Median :-1.000
## Mean :-0.347 Mean :-0.614 Mean :-0.8 Mean :-0.918
## 3rd Qu.: 0.515 3rd Qu.:-0.584 3rd Qu.:-1.0 3rd Qu.:-1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.0 Max. : 1.000
## V33 V34 V35 V36
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.979 Mean :-0.983 Mean :-0.927 Mean :-0.801
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000
## Max. : 0.997 Max. : 0.938 Max. : 1.000 Max. : 1.000
## V37 V38 V39 V40
## Min. :-1.000 Min. :-1.000 Min. :-1.0000 Min. :-1.0000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.0000 1st Qu.:-0.8700
## Median :-1.000 Median :-0.683 Median :-0.0220 Median : 0.1650
## Mean :-0.550 Mean :-0.233 Mean :-0.0235 Mean : 0.0661
## 3rd Qu.:-0.242 3rd Qu.: 0.739 3rd Qu.: 0.9560 3rd Qu.: 0.9780
## Max. : 1.000 Max. : 1.000 Max. : 1.0000 Max. : 1.0000
## V41 V42 V43 V44
## Min. :-1.000 Min. :-1.000 Min. :-1.0000 Min. :-1.0000
## 1st Qu.:-0.714 1st Qu.:-0.842 1st Qu.:-0.9670 1st Qu.:-1.0000
## Median : 0.539 Median : 0.461 Median :-0.0990 Median :-0.2340
## Mean : 0.211 Mean : 0.161 Mean :-0.0353 Mean :-0.0863
## 3rd Qu.: 1.000 3rd Qu.: 1.000 3rd Qu.: 0.8970 3rd Qu.: 0.9070
## Max. : 1.000 Max. : 1.000 Max. : 1.0000 Max. : 1.0000
## V45 V46 V47 V48
## Min. :-1.000 Min. :-1.000 Min. :-1.00 Min. :-1.0
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.00 1st Qu.:-1.0
## Median :-0.608 Median :-1.000 Median :-1.00 Median :-1.0
## Mean :-0.214 Mean :-0.497 Mean :-0.76 Mean :-0.9
## 3rd Qu.: 0.762 3rd Qu.: 0.010 3rd Qu.:-1.00 3rd Qu.:-1.0
## Max. : 1.000 Max. : 1.000 Max. : 1.00 Max. : 1.0
## V49 V50 V51 V52
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.972 Mean :-0.977 Mean :-0.909 Mean :-0.749
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-0.985
## Max. : 0.937 Max. : 0.862 Max. : 1.000 Max. : 1.000
## V53 V54 V55 V56
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-0.613 Median :-0.369 Median :-0.454
## Mean :-0.464 Mean :-0.190 Mean :-0.130 Mean :-0.177
## 3rd Qu.: 0.113 3rd Qu.: 0.853 3rd Qu.: 0.901 3rd Qu.: 0.780
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V57 V58 V59 V60
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.352 Median :-0.544 Median :-0.645 Median :-0.473
## Mean :-0.113 Mean :-0.178 Mean :-0.271 Mean :-0.177
## 3rd Qu.: 0.949 3rd Qu.: 0.878 3rd Qu.: 0.556 3rd Qu.: 0.815
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V61 V62 V63 V64
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.0
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.0
## Median :-0.571 Median :-1.000 Median :-1.000 Median :-1.0
## Mean :-0.185 Mean :-0.435 Mean :-0.727 Mean :-0.9
## 3rd Qu.: 0.851 3rd Qu.: 0.233 3rd Qu.:-0.972 3rd Qu.:-1.0
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.0
## V65 V66 V67 V68
## Min. :-1.000 Min. :-1.000 Min. :-1.00 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.00 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.00 Median :-1.000
## Mean :-0.977 Mean :-0.974 Mean :-0.89 Mean :-0.705
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.00 3rd Qu.:-0.920
## Max. : 0.908 Max. : 0.901 Max. : 1.00 Max. : 1.000
## V69 V70 V71 V72
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-0.726 Median :-0.714 Median :-0.840
## Mean :-0.424 Mean :-0.217 Mean :-0.241 Mean :-0.333
## 3rd Qu.: 0.290 3rd Qu.: 0.841 3rd Qu.: 0.751 3rd Qu.: 0.480
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V73 V74 V75 V76
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.945 Median :-0.947 Median :-0.811 Median :-0.629
## Mean :-0.304 Mean :-0.340 Mean :-0.348 Mean :-0.200
## 3rd Qu.: 0.707 3rd Qu.: 0.561 3rd Qu.: 0.366 3rd Qu.: 0.831
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V77 V78 V79 V80
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.585 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.182 Mean :-0.416 Mean :-0.704 Mean :-0.893
## 3rd Qu.: 0.865 3rd Qu.: 0.348 3rd Qu.:-0.951 3rd Qu.:-1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V81 V82 V83 V84
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.981 Mean :-0.970 Mean :-0.863 Mean :-0.670
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-0.877
## Max. : 0.997 Max. : 0.993 Max. : 1.000 Max. : 1.000
## V85 V86 V87 V88
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-0.836 Median :-0.846 Median :-0.903
## Mean :-0.423 Mean :-0.255 Mean :-0.278 Mean :-0.366
## 3rd Qu.: 0.340 3rd Qu.: 0.761 3rd Qu.: 0.687 3rd Qu.: 0.383
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V89 V90 V91 V92
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.943 Median :-0.925 Median :-0.727 Median :-0.604
## Mean :-0.334 Mean :-0.315 Mean :-0.289 Mean :-0.197
## 3rd Qu.: 0.566 3rd Qu.: 0.655 3rd Qu.: 0.558 3rd Qu.: 0.834
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V93 V94 V95 V96
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.766 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.230 Mean :-0.450 Mean :-0.701 Mean :-0.876
## 3rd Qu.: 0.809 3rd Qu.: 0.179 3rd Qu.:-0.973 3rd Qu.:-1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V97 V98 V99 V100
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.978 Mean :-0.960 Mean :-0.831 Mean :-0.650
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-0.846
## Max. : 0.998 Max. : 0.997 Max. : 1.000 Max. : 1.000
## V101 V102 V103 V104
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-0.921 Median :-0.859 Median :-0.827
## Mean :-0.443 Mean :-0.294 Mean :-0.283 Mean :-0.319
## 3rd Qu.: 0.280 3rd Qu.: 0.678 3rd Qu.: 0.675 3rd Qu.: 0.520
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V105 V106 V107 V108
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.836 Median :-0.726 Median :-0.519 Median :-0.583
## Mean :-0.240 Mean :-0.185 Mean :-0.199 Mean :-0.190
## 3rd Qu.: 0.812 3rd Qu.: 0.916 3rd Qu.: 0.787 3rd Qu.: 0.851
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V109 V110 V111 V112
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.00
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.00
## Median :-0.890 Median :-1.000 Median :-1.000 Median :-1.00
## Mean :-0.290 Mean :-0.494 Mean :-0.698 Mean :-0.85
## 3rd Qu.: 0.693 3rd Qu.: 0.004 3rd Qu.:-0.962 3rd Qu.:-1.00
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.00
## V113 V114 V115 V116
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.961 Mean :-0.943 Mean :-0.808 Mean :-0.642
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-0.851
## Max. : 0.994 Max. : 0.977 Max. : 1.000 Max. : 1.000
## V117 V118 V119 V120
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-0.999 Median :-0.959 Median :-0.808
## Mean :-0.461 Mean :-0.345 Mean :-0.330 Mean :-0.315
## 3rd Qu.: 0.209 3rd Qu.: 0.601 3rd Qu.: 0.590 3rd Qu.: 0.494
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V121 V122 V123 V124
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.684 Median :-0.380 Median :-0.345 Median :-0.579
## Mean :-0.188 Mean :-0.121 Mean :-0.138 Mean :-0.186
## 3rd Qu.: 0.912 3rd Qu.: 0.955 3rd Qu.: 0.841 3rd Qu.: 0.852
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V125 V126 V127 V128
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.909 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.319 Mean :-0.502 Mean :-0.674 Mean :-0.818
## 3rd Qu.: 0.571 3rd Qu.:-0.019 3rd Qu.:-0.904 3rd Qu.:-1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V129 V130 V131 V132
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.938 Mean :-0.923 Mean :-0.788 Mean :-0.633
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-0.785
## Max. : 0.991 Max. : 0.999 Max. : 1.000 Max. : 1.000
## V133 V134 V135 V136
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-0.962
## Mean :-0.486 Mean :-0.413 Mean :-0.431 Mean :-0.395
## 3rd Qu.: 0.054 3rd Qu.: 0.382 3rd Qu.: 0.256 3rd Qu.: 0.290
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V137 V138 V139 V140
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.822 Median :-0.322 Median :-0.293 Median :-0.600
## Mean :-0.221 Mean :-0.115 Mean :-0.117 Mean :-0.196
## 3rd Qu.: 0.878 3rd Qu.: 0.934 3rd Qu.: 0.865 3rd Qu.: 0.834
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V141 V142 V143 V144
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.918 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.331 Mean :-0.471 Mean :-0.622 Mean :-0.777
## 3rd Qu.: 0.551 3rd Qu.: 0.128 3rd Qu.:-0.704 3rd Qu.:-1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V145 V146 V147 V148
## Min. :-1.000 Min. :-1.000 Min. :-1.00 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.00 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.00 Median :-1.000
## Mean :-0.916 Mean :-0.909 Mean :-0.76 Mean :-0.606
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.00 3rd Qu.:-0.627
## Max. : 0.988 Max. : 1.000 Max. : 1.00 Max. : 1.000
## V149 V150 V151 V152
## Min. :-1.0000 Min. :-1.000 Min. :-1.000 Min. :-1.0000
## 1st Qu.:-1.0000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.0000
## Median :-1.0000 Median :-1.000 Median :-1.000 Median :-1.0000
## Mean :-0.4984 Mean :-0.477 Mean :-0.523 Mean :-0.4760
## 3rd Qu.: 0.0145 3rd Qu.: 0.113 3rd Qu.:-0.102 3rd Qu.: 0.0435
## Max. : 1.0000 Max. : 1.000 Max. : 1.000 Max. : 1.0000
## V153 V154 V155 V156
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.930 Median :-0.269 Median :-0.314 Median :-0.717
## Mean :-0.273 Mean :-0.108 Mean :-0.121 Mean :-0.249
## 3rd Qu.: 0.790 3rd Qu.: 0.911 3rd Qu.: 0.884 3rd Qu.: 0.696
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V157 V158 V159 V160
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.955 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.356 Mean :-0.435 Mean :-0.573 Mean :-0.744
## 3rd Qu.: 0.464 3rd Qu.: 0.273 3rd Qu.:-0.406 3rd Qu.:-1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V161 V162 V163 V164
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.906 Mean :-0.897 Mean :-0.724 Mean :-0.567
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-0.375
## Max. : 0.997 Max. : 0.990 Max. : 1.000 Max. : 1.000
## V165 V166 V167 V168
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.478 Mean :-0.496 Mean :-0.570 Mean :-0.517
## 3rd Qu.: 0.118 3rd Qu.: 0.034 3rd Qu.:-0.333 3rd Qu.:-0.090
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V169 V170 V171 V172
## Min. :-1.000 Min. :-1.0000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.0000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.915 Median :-0.1570 Median :-0.412 Median :-0.864
## Mean :-0.283 Mean :-0.0755 Mean :-0.155 Mean :-0.320
## 3rd Qu.: 0.756 3rd Qu.: 0.9485 3rd Qu.: 0.824 3rd Qu.: 0.535
## Max. : 1.000 Max. : 1.0000 Max. : 1.000 Max. : 1.000
## V173 V174 V175 V176
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.983 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.356 Mean :-0.381 Mean :-0.530 Mean :-0.735
## 3rd Qu.: 0.509 3rd Qu.: 0.506 3rd Qu.:-0.155 3rd Qu.:-1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V177 V178 V179 V180
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.0000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.0000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.0000
## Mean :-0.914 Mean :-0.904 Mean :-0.711 Mean :-0.5216
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-0.0985
## Max. : 0.969 Max. : 0.966 Max. : 1.000 Max. : 1.0000
## V181 V182 V183 V184
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.0000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.0000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.0000
## Mean :-0.421 Mean :-0.458 Mean :-0.562 Mean :-0.5104
## 3rd Qu.: 0.400 3rd Qu.: 0.211 3rd Qu.:-0.304 3rd Qu.:-0.0725
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.0000
## V185 V186 V187 V188
## Min. :-1.000 Min. :-1.0000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.0000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.774 Median :-0.0970 Median :-0.482 Median :-0.861
## Mean :-0.247 Mean :-0.0564 Mean :-0.198 Mean :-0.331
## 3rd Qu.: 0.789 3rd Qu.: 0.9710 3rd Qu.: 0.707 3rd Qu.: 0.471
## Max. : 1.000 Max. : 1.0000 Max. : 1.000 Max. : 1.000
## V189 V190 V191 V192
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.945 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.305 Mean :-0.331 Mean :-0.526 Mean :-0.758
## 3rd Qu.: 0.674 3rd Qu.: 0.680 3rd Qu.:-0.121 3rd Qu.:-1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V193 V194 V195 V196
## Min. :-1.000 Min. :-1.000 Min. :-1.00 Min. :-1.0000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.00 1st Qu.:-1.0000
## Median :-1.000 Median :-1.000 Median :-1.00 Median :-1.0000
## Mean :-0.931 Mean :-0.930 Mean :-0.75 Mean :-0.5253
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.00 3rd Qu.:-0.0935
## Max. : 0.965 Max. : 0.975 Max. : 1.00 Max. : 1.0000
## V197 V198 V199 V200
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-0.898
## Mean :-0.356 Mean :-0.347 Mean :-0.447 Mean :-0.407
## 3rd Qu.: 0.603 3rd Qu.: 0.598 3rd Qu.: 0.211 3rd Qu.: 0.203
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V201 V202 V203 V204
## Min. :-1.000 Min. :-1.0000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.0000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.359 Median : 0.1330 Median :-0.372 Median :-0.667
## Mean :-0.125 Mean : 0.0203 Mean :-0.160 Mean :-0.220
## 3rd Qu.: 0.925 3rd Qu.: 0.9840 3rd Qu.: 0.720 3rd Qu.: 0.798
## Max. : 1.000 Max. : 1.0000 Max. : 1.000 Max. : 1.000
## V205 V206 V207 V208
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.784 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.208 Mean :-0.337 Mean :-0.584 Mean :-0.816
## 3rd Qu.: 0.883 3rd Qu.: 0.669 3rd Qu.:-0.436 3rd Qu.:-1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V209 V210 V211 V212
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.00
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.00
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.00
## Mean :-0.950 Mean :-0.958 Mean :-0.834 Mean :-0.62
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-0.58
## Max. : 0.987 Max. : 0.979 Max. : 1.000 Max. : 1.00
## V213 V214 V215 V216
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-0.802 Median :-0.633 Median :-0.236
## Mean :-0.370 Mean :-0.217 Mean :-0.197 Mean :-0.106
## 3rd Qu.: 0.529 3rd Qu.: 0.872 3rd Qu.: 0.844 3rd Qu.: 0.798
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V217 V218 V219 V220
## Min. :-1.000 Min. :-1.000 Min. :-1.0000 Min. :-1.0000
## 1st Qu.:-0.766 1st Qu.:-0.521 1st Qu.:-0.8960 1st Qu.:-1.0000
## Median : 0.428 Median : 0.567 Median : 0.0650 Median :-0.1840
## Mean : 0.172 Mean : 0.256 Mean : 0.0375 Mean :-0.0768
## 3rd Qu.: 1.000 3rd Qu.: 1.000 3rd Qu.: 0.9660 3rd Qu.: 0.9710
## Max. : 1.000 Max. : 1.000 Max. : 1.0000 Max. : 1.0000
## V221 V222 V223 V224
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.837 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.222 Mean :-0.471 Mean :-0.723 Mean :-0.882
## 3rd Qu.: 0.855 3rd Qu.: 0.141 3rd Qu.:-0.954 3rd Qu.:-1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V225 V226 V227 V228
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.965 Mean :-0.982 Mean :-0.926 Mean :-0.805
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000
## Max. : 0.948 Max. : 0.946 Max. : 1.000 Max. : 1.000
## V229 V230 V231 V232
## Min. :-1.000 Min. :-1.000 Min. :-1.0000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.0000 1st Qu.:-0.834
## Median :-1.000 Median :-0.980 Median :-0.1510 Median : 0.500
## Mean :-0.583 Mean :-0.297 Mean :-0.0652 Mean : 0.181
## 3rd Qu.:-0.383 3rd Qu.: 0.672 3rd Qu.: 0.9790 3rd Qu.: 1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.0000 Max. : 1.000
## V233 V234 V235 V236
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.: 0.127 1st Qu.: 0.135 1st Qu.:-0.934 1st Qu.:-1.000
## Median : 0.946 Median : 0.933 Median : 0.443 Median :-0.647
## Mean : 0.490 Mean : 0.486 Mean : 0.138 Mean :-0.180
## 3rd Qu.: 1.000 3rd Qu.: 1.000 3rd Qu.: 1.000 3rd Qu.: 0.890
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V237 V238 V239 V240
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.475 Mean :-0.720 Mean :-0.869 Mean :-0.939
## 3rd Qu.: 0.103 3rd Qu.:-0.904 3rd Qu.:-1.000 3rd Qu.:-1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V241 V242 V243 V244
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.980 Mean :-0.997 Mean :-0.983 Mean :-0.951
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000
## Max. : 0.853 Max. : 0.376 Max. : 1.000 Max. : 1.000
## V245 V246 V247 V248
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-0.353
## Mean :-0.878 Mean :-0.734 Mean :-0.502 Mean :-0.199
## 3rd Qu.:-1.000 3rd Qu.:-0.781 3rd Qu.: 0.012 3rd Qu.: 0.554
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V249 V250 V251 V252
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-0.661 1st Qu.:-0.738 1st Qu.:-1.000 1st Qu.:-1.000
## Median : 0.368 Median : 0.336 Median :-0.579 Median :-1.000
## Mean : 0.140 Mean : 0.116 Mean :-0.314 Mean :-0.654
## 3rd Qu.: 0.835 3rd Qu.: 0.815 3rd Qu.: 0.346 3rd Qu.:-0.504
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V253 V254 V255 V256
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.838 Mean :-0.922 Mean :-0.957 Mean :-0.979
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V257
## Min. :-1.000
## 1st Qu.:-1.000
## Median :-1.000
## Mean :-0.995
## 3rd Qu.:-1.000
## Max. : 0.592
sapply(DATASET.train[1, ], class)
## V1 V2 V3 V4 V5 V6 V7
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V8 V9 V10 V11 V12 V13 V14
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V15 V16 V17 V18 V19 V20 V21
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V22 V23 V24 V25 V26 V27 V28
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V29 V30 V31 V32 V33 V34 V35
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V36 V37 V38 V39 V40 V41 V42
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V43 V44 V45 V46 V47 V48 V49
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V50 V51 V52 V53 V54 V55 V56
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V57 V58 V59 V60 V61 V62 V63
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V64 V65 V66 V67 V68 V69 V70
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V71 V72 V73 V74 V75 V76 V77
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V78 V79 V80 V81 V82 V83 V84
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V85 V86 V87 V88 V89 V90 V91
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V92 V93 V94 V95 V96 V97 V98
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V99 V100 V101 V102 V103 V104 V105
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V106 V107 V108 V109 V110 V111 V112
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V113 V114 V115 V116 V117 V118 V119
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V120 V121 V122 V123 V124 V125 V126
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V127 V128 V129 V130 V131 V132 V133
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V134 V135 V136 V137 V138 V139 V140
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V141 V142 V143 V144 V145 V146 V147
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V148 V149 V150 V151 V152 V153 V154
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V155 V156 V157 V158 V159 V160 V161
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V162 V163 V164 V165 V166 V167 V168
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V169 V170 V171 V172 V173 V174 V175
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V176 V177 V178 V179 V180 V181 V182
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V183 V184 V185 V186 V187 V188 V189
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V190 V191 V192 V193 V194 V195 V196
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V197 V198 V199 V200 V201 V202 V203
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V204 V205 V206 V207 V208 V209 V210
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V211 V212 V213 V214 V215 V216 V217
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V218 V219 V220 V221 V222 V223 V224
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V225 V226 V227 V228 V229 V230 V231
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V232 V233 V234 V235 V236 V237 V238
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V239 V240 V241 V242 V243 V244 V245
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V246 V247 V248 V249 V250 V251 V252
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V253 V254 V255 V256 V257
## "numeric" "numeric" "numeric" "numeric" "numeric"
head(DATASET.test)
## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13
## 1 9 -1 -1 -1 -1.000 -1.0 -0.948 -0.561 0.148 0.384 0.904 0.290 -0.782
## 2 6 -1 -1 -1 -1.000 -1.0 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
## 3 3 -1 -1 -1 -0.593 0.7 1.000 1.000 1.000 1.000 0.853 0.075 -0.925
## 4 6 -1 -1 -1 -1.000 -1.0 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
## 5 6 -1 -1 -1 -1.000 -1.0 -1.000 -1.000 -0.858 -0.106 0.802 -0.210 -1.000
## 6 0 -1 -1 -1 -1.000 -1.0 -1.000 0.195 1.000 0.054 -1.000 -1.000 -1.000
## V14 V15 V16 V17 V18 V19 V20 V21 V22 V23 V24 V25 V26
## 1 -1 -1 -1 -1 -1 -1 -1.000 -1.000 -0.748 0.588 1.000 1.000 0.991
## 2 -1 -1 -1 -1 -1 -1 -1.000 -1.000 -1.000 -0.783 -0.973 -1.000 -1.000
## 3 -1 -1 -1 -1 -1 -1 -0.553 0.998 1.000 1.000 1.000 1.000 1.000
## 4 -1 -1 -1 -1 -1 -1 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
## 5 -1 -1 -1 -1 -1 -1 -1.000 -1.000 -1.000 -1.000 -0.854 0.597 1.000
## 6 -1 -1 -1 -1 -1 -1 -1.000 -1.000 -1.000 -0.801 0.790 1.000 0.856
## V27 V28 V29 V30 V31 V32 V33 V34 V35 V36 V37 V38
## 1 0.915 1.000 0.931 -0.476 -1.000 -1 -1 -1 -1 -1.000 -0.787 0.794
## 2 -1.000 -1.000 -1.000 -1.000 -1.000 -1 -1 -1 -1 -1.000 -1.000 -0.364
## 3 1.000 1.000 0.961 -0.076 -0.999 -1 -1 -1 -1 0.228 1.000 0.849
## 4 -1.000 -1.000 -1.000 -1.000 -1.000 -1 -1 -1 -1 -1.000 -1.000 -1.000
## 5 0.798 -0.388 -1.000 -1.000 -1.000 -1 -1 -1 -1 -1.000 -1.000 -1.000
## 6 -0.282 -0.831 -1.000 -1.000 -1.000 -1 -1 -1 -1 -1.000 -1.000 -0.937
## V39 V40 V41 V42 V43 V44 V45 V46 V47 V48 V49
## 1 1.000 0.727 -0.178 -0.693 -0.786 -0.624 0.834 0.756 -0.822 -1 -1
## 2 0.789 -0.371 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1 -1
## 3 -0.150 -0.705 -1.000 -0.850 -0.333 -0.072 0.929 1.000 -0.451 -1 -1
## 4 -0.417 -0.330 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1 -1
## 5 -0.481 0.600 1.000 0.653 -0.780 -1.000 -1.000 -1.000 -1.000 -1 -1
## 6 0.498 1.000 0.975 -0.232 0.932 0.926 -0.069 -0.965 -1.000 -1 -1
## V50 V51 V52 V53 V54 V55 V56 V57 V58 V59 V60
## 1 -1 -1 -0.922 0.810 1.000 0.010 -0.928 -1.000 -1.000 -1.000 -1
## 2 -1 -1 -1.000 -0.467 0.963 0.609 -0.986 -1.000 -1.000 -1.000 -1
## 3 -1 -1 -0.586 0.777 -0.524 -1.000 -1.000 -1.000 -1.000 -1.000 -1
## 4 -1 -1 -1.000 -0.883 0.447 0.999 0.775 -1.000 -1.000 -1.000 -1
## 5 -1 -1 -1.000 -1.000 -0.386 0.913 1.000 0.658 -0.825 -1.000 -1
## 6 -1 -1 -1.000 -1.000 0.070 1.000 1.000 0.446 -0.953 -0.385 1
## V61 V62 V63 V64 V65 V66 V67 V68 V69 V70 V71
## 1 -0.390 1.000 0.271 -1.000 -1 -1 -1 0.012 1.000 0.248 -1.000
## 2 -1.000 -1.000 -1.000 -1.000 -1 -1 -1 -0.875 0.605 0.960 -0.351
## 3 0.344 1.000 0.544 -0.999 -1 -1 -1 -1.000 -1.000 -1.000 -1.000
## 4 -1.000 -1.000 -1.000 -1.000 -1 -1 -1 -0.589 0.803 1.000 0.602
## 5 -1.000 -1.000 -1.000 -1.000 -1 -1 -1 -1.000 -0.373 0.939 0.999
## 6 1.000 0.276 -0.895 -1.000 -1 -1 -1 -1.000 -0.423 0.960 1.000
## V72 V73 V74 V75 V76 V77 V78 V79 V80 V81 V82 V83
## 1 -1.000 -1.000 -1 -1.000 -0.402 0.326 1 0.801 -0.998 -1 -1 -0.981
## 2 -1.000 -1.000 -1 -1.000 -1.000 -1.000 -1 -1.000 -1.000 -1 -1 -1.000
## 3 -1.000 -1.000 -1 -1.000 -0.803 0.930 1 0.650 -0.999 -1 -1 -1.000
## 4 0.257 -1.000 -1 -1.000 -1.000 -1.000 -1 -1.000 -1.000 -1 -1 -0.588
## 5 0.129 -0.835 -1 -1.000 -1.000 -1.000 -1 -1.000 -1.000 -1 -1 -1.000
## 6 0.762 -0.799 -1 -0.987 -0.238 0.941 1 -0.288 -1.000 -1 -1 -1.000
## V84 V85 V86 V87 V88 V89 V90 V91 V92 V93 V94
## 1 0.645 1.000 -0.687 -1.000 -1.000 -1 -1 -0.792 0.976 1.000 1.000
## 2 0.050 1.000 0.096 -1.000 -1.000 -1 -1 -1.000 -1.000 -1.000 -1.000
## 3 -1.000 -1.000 -1.000 -1.000 -1.000 -1 -1 -0.579 0.821 1.000 1.000
## 4 0.862 0.981 0.150 -0.980 -1.000 -1 -1 -1.000 -1.000 -0.969 -0.437
## 5 -0.340 0.986 1.000 0.007 -1.000 -1 -1 -1.000 -1.000 -1.000 -1.000
## 6 -0.989 0.321 1.000 0.924 -0.512 -1 -1 -1.000 -1.000 -0.015 1.000
## V95 V96 V97 V98 V99 V100 V101 V102 V103 V104
## 1 0.413 -0.976 -1.000 -1.000 -0.993 0.834 0.897 -0.951 -1.000 -1
## 2 -1.000 -1.000 -1.000 -1.000 -0.582 0.970 0.532 -0.922 -1.000 -1
## 3 -0.131 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1
## 4 -0.295 -0.295 -0.939 -0.651 0.863 0.965 -0.219 -1.000 -1.000 -1
## 5 -1.000 -1.000 -1.000 -1.000 -0.741 0.763 1.000 0.119 -0.986 -1
## 6 0.906 -0.758 -1.000 -1.000 -1.000 -0.530 0.992 1.000 -0.055 -1
## V105 V106 V107 V108 V109 V110 V111 V112 V113 V114
## 1 -1.000 -0.831 0.140 1.000 1.000 0.302 -0.889 -1.000 -1.000 -1.000
## 2 -1.000 -1.000 -1.000 -1.000 -0.602 0.307 0.718 0.718 -0.373 -0.998
## 3 -0.621 0.156 0.934 1.000 1.000 0.575 -0.933 -1.000 -1.000 -1.000
## 4 -1.000 -1.000 -0.946 -0.592 0.488 1.000 1.000 1.000 0.251 0.338
## 5 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
## 6 -1.000 -1.000 -1.000 -1.000 -0.651 0.810 1.000 0.206 -1.000 -1.000
## V115 V116 V117 V118 V119 V120 V121 V122 V123 V124
## 1 -1.000 0.356 0.794 -0.836 -1.000 -0.445 0.074 0.833 1.000 1.000
## 2 0.723 1.000 -0.431 -1.000 -1.000 -1.000 -1.000 -1.000 -0.817 -0.136
## 3 -1.000 -1.000 -1.000 -0.952 -0.176 0.602 1.000 1.000 1.000 0.952
## 4 1.000 0.054 -1.000 -1.000 -1.000 -1.000 -1.000 -0.698 0.628 1.000
## 5 0.271 1.000 0.879 -0.871 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
## 6 -1.000 0.424 1.000 0.868 -0.717 -1.000 -1.000 -1.000 -1.000 -1.000
## V125 V126 V127 V128 V129 V130 V131 V132 V133 V134
## 1 0.696 -0.881 -1.000 -1.000 -1.000 -1.000 -1.000 -0.368 0.955 1.00
## 2 0.808 1.000 1.000 1.000 0.697 -0.670 0.965 0.659 -1.000 -1.00
## 3 -0.093 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.41
## 4 1.000 0.673 0.059 0.890 0.995 0.995 0.878 -0.814 -1.000 -1.00
## 5 -1.000 -1.000 -1.000 -1.000 -1.000 -0.620 0.933 0.983 -0.349 -1.00
## 6 -0.989 0.721 1.000 -0.125 -1.000 -1.000 -0.944 0.867 1.000 0.00
## V135 V136 V137 V138 V139 V140 V141 V142 V143 V144 V145
## 1 1 1 1 0.905 1.000 1.000 -0.262 -1.000 -1.000 -1.000 -1.000
## 2 -1 -1 -1 -0.512 0.738 1.000 0.839 -0.336 -0.977 0.433 0.878
## 3 1 1 1 1.000 1.000 0.792 -0.715 -1.000 -1.000 -1.000 -1.000
## 4 -1 -1 -1 0.388 1.000 0.711 -0.501 -0.926 -0.672 0.963 0.473
## 5 -1 -1 -1 -1.000 -1.000 -0.930 -0.356 0.552 0.647 0.618 -0.633
## 6 -1 -1 -1 -1.000 -1.000 -1.000 -1.000 0.579 1.000 0.344 -1.000
## V146 V147 V148 V149 V150 V151 V152 V153 V154 V155
## 1 -1.000 -1.000 -1.000 -0.507 0.451 0.692 0.692 -0.007 -0.237 1.000
## 2 0.161 1.000 -0.102 -1.000 -1.000 -1.000 -1.000 -0.643 0.870 0.970
## 3 -1.000 -1.000 -1.000 -1.000 -1.000 -0.345 0.333 -0.050 -0.333 -0.172
## 4 0.989 0.775 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 0.677 1.000
## 5 0.148 1.000 0.549 -1.000 -1.000 -1.000 -1.000 -1.000 -0.853 -0.172
## 6 -1.000 -0.111 0.996 0.978 -0.604 -1.000 -1.000 -1.000 -1.000 -1.000
## V156 V157 V158 V159 V160 V161 V162 V163 V164 V165
## 1 0.882 -0.795 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
## 2 0.264 -0.971 -1.000 -0.399 1.000 0.117 0.835 0.968 -0.701 -1.000
## 3 0.606 0.950 -0.101 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
## 4 -0.654 -1.000 -1.000 0.122 1.000 -0.357 0.970 0.922 -0.506 -0.994
## 5 0.666 1.000 1.000 1.000 1.000 0.821 0.861 1.000 0.086 -1.000
## 6 -1.000 -1.000 0.111 1.000 0.013 -1.000 -1.000 0.406 1.000 0.988
## V166 V167 V168 V169 V170 V171 V172 V173 V174 V175 V176
## 1 -1.000 -1 -1 -1.000 0.155 1.000 0.436 -1.000 -1.000 -1.000 -1.000
## 2 -1.000 -1 -1 0.198 1.000 0.052 -1.000 -1.000 -0.291 0.876 0.790
## 3 -1.000 -1 -1 -1.000 -1.000 -1.000 -0.838 0.548 1.000 -0.266 -1.000
## 4 -1.000 -1 -1 -1.000 0.440 0.262 -0.996 -0.985 -0.323 0.986 0.668
## 5 -1.000 -1 -1 -0.280 0.878 1.000 1.000 0.806 -0.097 -0.191 1.000
## 6 -0.861 -1 -1 -1.000 -1.000 -1.000 -1.000 -1.000 0.253 1.000 0.460
## V177 V178 V179 V180 V181 V182 V183 V184 V185 V186
## 1 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.991 0.703
## 2 -0.819 0.392 0.962 -0.461 -1.000 -1.000 -1.000 -0.948 0.820 1.000
## 3 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
## 4 -0.954 0.053 1.000 1.000 0.499 0.038 0.179 -0.215 -0.215 -0.215
## 5 0.695 0.909 1.000 -0.666 -1.000 -1.000 -1.000 -0.316 0.999 1.000
## 6 -1.000 -1.000 0.148 1.000 0.811 -0.888 -1.000 -1.000 -1.000 -1.000
## V187 V188 V189 V190 V191 V192 V193 V194 V195 V196
## 1 1.000 -0.025 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
## 2 -0.168 -0.475 0.280 0.968 0.880 -0.613 -1.000 -0.551 0.854 0.980
## 3 -1.000 -1.000 -0.285 1.000 0.729 -1.000 -1.000 -1.000 -1.000 -0.611
## 4 -0.215 0.184 0.637 1.000 0.443 -0.741 -1.000 -0.979 0.189 0.942
## 5 0.712 -0.530 -0.995 -0.859 0.569 1.000 -0.267 0.955 1.000 -0.552
## 6 -1.000 -1.000 -0.872 0.456 1.000 0.171 -1.000 -1.000 -0.465 0.913
## V197 V198 V199 V200 V201 V202 V203 V204 V205 V206
## 1 -1.000 -1.000 -1.000 -1.000 -0.833 0.959 1.000 -0.629 -1.000 -1.000
## 2 0.498 0.324 0.324 0.328 0.998 1.000 0.970 0.995 0.976 0.250
## 3 -0.944 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.556 0.943 1.000
## 4 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.896 0.177
## 5 -1.000 -1.000 -1.000 0.646 1.000 0.317 -0.926 -1.000 -0.849 0.598
## 6 1.000 0.281 -0.754 -1.000 -1.000 -1.000 -0.740 -0.436 0.657 1.000
## V207 V208 V209 V210 V211 V212 V213 V214 V215 V216
## 1 -1.000 -1.000 -1.00 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
## 2 -0.642 -1.000 -1.00 -1.000 -0.640 0.661 0.971 1.000 1.000 1.000
## 3 0.779 -0.943 -1.00 -1.000 -0.943 0.779 0.555 -0.333 -0.333 -0.333
## 4 -0.911 -1.000 -1.00 -1.000 -1.000 -0.723 -0.451 -0.081 -0.611 -0.021
## 5 1.000 0.169 -0.97 0.631 1.000 0.754 0.046 -0.244 -0.661 0.984
## 6 1.000 0.008 -1.00 -1.000 -1.000 -0.006 0.976 1.000 0.868 0.744
## V217 V218 V219 V220 V221 V222 V223 V224 V225 V226 V227
## 1 -0.600 0.998 0.841 -0.932 -1.000 -1 -1.000 -1.000 -1 -1.000 -1.000
## 2 0.950 0.774 0.774 0.302 -0.522 -1 -1.000 -1.000 -1 -1.000 -1.000
## 3 -0.166 0.389 1.000 1.000 1.000 1 0.497 -1.000 -1 -1.000 -1.000
## 4 -0.414 -0.021 -0.182 -0.648 -0.780 -1 -1.000 -1.000 -1 -1.000 -1.000
## 5 1.000 0.142 -0.584 0.075 0.833 1 0.123 -0.963 -1 -0.537 0.896
## 6 0.744 0.744 0.850 1.000 1.000 1 0.782 -0.736 -1 -1.000 -1.000
## V228 V229 V230 V231 V232 V233 V234 V235 V236 V237 V238
## 1 -1.000 -1.000 -1.000 -1.000 -1.000 -0.424 1 0.732 -1 -1.00 -1.000
## 2 -1.000 -0.663 -0.606 -0.606 -0.606 -0.688 -1 -1.000 -1 -1.00 -1.000
## 3 0.507 1.000 1.000 1.000 1.000 1.000 1 1.000 1 0.83 0.053
## 4 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1 -1.000 -1 -1.00 -1.000
## 5 1.000 1.000 1.000 1.000 1.000 1.000 1 1.000 1 0.83 -0.387
## 6 -1.000 -0.310 0.686 1.000 1.000 1.000 1 1.000 1 1.00 0.602
## V239 V240 V241 V242 V243 V244 V245 V246 V247 V248 V249
## 1 -1.000 -1 -1 -1 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.908
## 2 -1.000 -1 -1 -1 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
## 3 -0.946 -1 -1 -1 -1.000 -0.941 0.059 0.615 1.000 1.000 0.717
## 4 -1.000 -1 -1 -1 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
## 5 -0.976 -1 -1 -1 -0.697 -0.108 0.312 0.901 0.901 0.901 0.901
## 6 -0.906 -1 -1 -1 -1.000 -1.000 -1.000 -0.903 0.009 0.224 1.000
## V250 V251 V252 V253 V254 V255 V256 V257
## 1 0.430 0.622 -0.973 -1.000 -1.000 -1 -1 -1
## 2 -1.000 -1.000 -1.000 -1.000 -1.000 -1 -1 -1
## 3 0.333 0.162 -0.393 -1.000 -1.000 -1 -1 -1
## 4 -1.000 -1.000 -1.000 -1.000 -1.000 -1 -1 -1
## 5 0.901 0.290 -0.369 -0.867 -1.000 -1 -1 -1
## 6 0.988 0.187 0.139 -0.641 -0.812 -1 -1 -1
dim(DATASET.test)
## [1] 2007 257
nrow(DATASET.test)
## [1] 2007
ncol(DATASET.test)
## [1] 257
colnames(DATASET.test)
## [1] "V1" "V2" "V3" "V4" "V5" "V6" "V7" "V8" "V9" "V10"
## [11] "V11" "V12" "V13" "V14" "V15" "V16" "V17" "V18" "V19" "V20"
## [21] "V21" "V22" "V23" "V24" "V25" "V26" "V27" "V28" "V29" "V30"
## [31] "V31" "V32" "V33" "V34" "V35" "V36" "V37" "V38" "V39" "V40"
## [41] "V41" "V42" "V43" "V44" "V45" "V46" "V47" "V48" "V49" "V50"
## [51] "V51" "V52" "V53" "V54" "V55" "V56" "V57" "V58" "V59" "V60"
## [61] "V61" "V62" "V63" "V64" "V65" "V66" "V67" "V68" "V69" "V70"
## [71] "V71" "V72" "V73" "V74" "V75" "V76" "V77" "V78" "V79" "V80"
## [81] "V81" "V82" "V83" "V84" "V85" "V86" "V87" "V88" "V89" "V90"
## [91] "V91" "V92" "V93" "V94" "V95" "V96" "V97" "V98" "V99" "V100"
## [101] "V101" "V102" "V103" "V104" "V105" "V106" "V107" "V108" "V109" "V110"
## [111] "V111" "V112" "V113" "V114" "V115" "V116" "V117" "V118" "V119" "V120"
## [121] "V121" "V122" "V123" "V124" "V125" "V126" "V127" "V128" "V129" "V130"
## [131] "V131" "V132" "V133" "V134" "V135" "V136" "V137" "V138" "V139" "V140"
## [141] "V141" "V142" "V143" "V144" "V145" "V146" "V147" "V148" "V149" "V150"
## [151] "V151" "V152" "V153" "V154" "V155" "V156" "V157" "V158" "V159" "V160"
## [161] "V161" "V162" "V163" "V164" "V165" "V166" "V167" "V168" "V169" "V170"
## [171] "V171" "V172" "V173" "V174" "V175" "V176" "V177" "V178" "V179" "V180"
## [181] "V181" "V182" "V183" "V184" "V185" "V186" "V187" "V188" "V189" "V190"
## [191] "V191" "V192" "V193" "V194" "V195" "V196" "V197" "V198" "V199" "V200"
## [201] "V201" "V202" "V203" "V204" "V205" "V206" "V207" "V208" "V209" "V210"
## [211] "V211" "V212" "V213" "V214" "V215" "V216" "V217" "V218" "V219" "V220"
## [221] "V221" "V222" "V223" "V224" "V225" "V226" "V227" "V228" "V229" "V230"
## [231] "V231" "V232" "V233" "V234" "V235" "V236" "V237" "V238" "V239" "V240"
## [241] "V241" "V242" "V243" "V244" "V245" "V246" "V247" "V248" "V249" "V250"
## [251] "V251" "V252" "V253" "V254" "V255" "V256" "V257"
summary(DATASET.test)
## V1 V2 V3 V4
## Min. :0.00 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:1.00 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :4.00 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :3.85 Mean :-0.997 Mean :-0.978 Mean :-0.949
## 3rd Qu.:6.00 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000
## Max. :9.00 Max. :-0.130 Max. : 1.000 Max. : 1.000
## V5 V6 V7 V8
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-0.714
## Mean :-0.883 Mean :-0.772 Mean :-0.590 Mean :-0.354
## 3rd Qu.:-1.000 3rd Qu.:-0.942 3rd Qu.:-0.306 3rd Qu.: 0.308
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V9 V10 V11 V12
## Min. :-1.0000 Min. :-1.0000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.0000 1st Qu.:-0.9970 1st Qu.:-1.000 1st Qu.:-1.000
## Median : 0.0350 Median :-0.0430 Median :-0.652 Median :-1.000
## Mean :-0.0618 Mean :-0.0762 Mean :-0.309 Mean :-0.518
## 3rd Qu.: 0.7045 3rd Qu.: 0.6700 3rd Qu.: 0.423 3rd Qu.:-0.036
## Max. : 1.0000 Max. : 1.0000 Max. : 1.000 Max. : 1.000
## V13 V14 V15 V16
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.703 Mean :-0.836 Mean :-0.914 Mean :-0.964
## 3rd Qu.:-0.734 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V17 V18 V19 V20
## Min. :-1.000 Min. :-1.000 Min. :-1.00 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.00 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.00 Median :-1.000
## Mean :-0.993 Mean :-0.987 Mean :-0.95 Mean :-0.863
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.00 3rd Qu.:-1.000
## Max. : 0.553 Max. : 0.964 Max. : 1.00 Max. : 1.000
## V21 V22 V23 V24
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-0.899
## Median :-1.000 Median :-1.000 Median :-0.419 Median : 0.422
## Mean :-0.685 Mean :-0.422 Mean :-0.142 Mean : 0.138
## 3rd Qu.:-0.868 3rd Qu.: 0.277 3rd Qu.: 0.909 3rd Qu.: 1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V25 V26 V27 V28
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-0.148 1st Qu.:-0.148 1st Qu.:-0.882 1st Qu.:-1.000
## Median : 0.873 Median : 0.840 Median : 0.361 Median :-0.308
## Mean : 0.410 Mean : 0.395 Mean : 0.143 Mean :-0.102
## 3rd Qu.: 1.000 3rd Qu.: 1.000 3rd Qu.: 1.000 3rd Qu.: 0.925
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V29 V30 V31 V32
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.995 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.366 Mean :-0.638 Mean :-0.819 Mean :-0.916
## 3rd Qu.: 0.496 3rd Qu.:-0.675 3rd Qu.:-1.000 3rd Qu.:-1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V33 V34 V35 V36
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.0
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.0
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.0
## Mean :-0.979 Mean :-0.979 Mean :-0.926 Mean :-0.8
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.0
## Max. : 0.901 Max. : 0.925 Max. : 1.000 Max. : 1.0
## V37 V38 V39 V40
## Min. :-1.000 Min. :-1.000 Min. :-1.0000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.0000 1st Qu.:-0.816
## Median :-1.000 Median :-0.625 Median : 0.0730 Median : 0.257
## Mean :-0.534 Mean :-0.204 Mean : 0.0148 Mean : 0.111
## 3rd Qu.:-0.185 3rd Qu.: 0.815 3rd Qu.: 0.9910 3rd Qu.: 1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.0000 Max. : 1.000
## V41 V42 V43 V44
## Min. :-1.000 Min. :-1.000 Min. :-1.0000 Min. :-1.0000
## 1st Qu.:-0.691 1st Qu.:-0.799 1st Qu.:-0.9135 1st Qu.:-1.0000
## Median : 0.632 Median : 0.556 Median : 0.0610 Median :-0.1470
## Mean : 0.237 Mean : 0.189 Mean : 0.0323 Mean :-0.0571
## 3rd Qu.: 1.000 3rd Qu.: 1.000 3rd Qu.: 0.9680 3rd Qu.: 0.9525
## Max. : 1.000 Max. : 1.000 Max. : 1.0000 Max. : 1.0000
## V45 V46 V47 V48
## Min. :-1.000 Min. :-1.0000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.0000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.660 Median :-1.0000 Median :-1.000 Median :-1.000
## Mean :-0.217 Mean :-0.5030 Mean :-0.749 Mean :-0.893
## 3rd Qu.: 0.800 3rd Qu.:-0.0495 3rd Qu.:-1.000 3rd Qu.:-1.000
## Max. : 1.000 Max. : 1.0000 Max. : 1.000 Max. : 1.000
## V49 V50 V51 V52
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.973 Mean :-0.977 Mean :-0.903 Mean :-0.734
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-0.945
## Max. : 0.997 Max. : 0.928 Max. : 1.000 Max. : 1.000
## V53 V54 V55 V56
## Min. :-1.000 Min. :-1.000 Min. :-1.0000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.0000 1st Qu.:-1.000
## Median :-1.000 Median :-0.387 Median :-0.1430 Median :-0.283
## Mean :-0.433 Mean :-0.134 Mean :-0.0657 Mean :-0.109
## 3rd Qu.: 0.233 3rd Qu.: 0.901 3rd Qu.: 0.9470 3rd Qu.: 0.853
## Max. : 1.000 Max. : 1.000 Max. : 1.0000 Max. : 1.000
## V57 V58 V59 V60
## Min. :-1.0000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.0000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.2000 Median :-0.330 Median :-0.470 Median :-0.374
## Mean :-0.0587 Mean :-0.105 Mean :-0.181 Mean :-0.124
## 3rd Qu.: 0.9890 3rd Qu.: 0.954 3rd Qu.: 0.758 3rd Qu.: 0.916
## Max. : 1.0000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V61 V62 V63 V64
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.420 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.144 Mean :-0.399 Mean :-0.704 Mean :-0.888
## 3rd Qu.: 0.891 3rd Qu.: 0.397 3rd Qu.:-0.883 3rd Qu.:-1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V65 V66 V67 V68
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.973 Mean :-0.979 Mean :-0.873 Mean :-0.661
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-0.781
## Max. : 0.930 Max. : 0.760 Max. : 1.000 Max. : 1.000
## V69 V70 V71 V72
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-0.514 Median :-0.529 Median :-0.692
## Mean :-0.372 Mean :-0.158 Mean :-0.177 Mean :-0.259
## 3rd Qu.: 0.477 3rd Qu.: 0.919 3rd Qu.: 0.866 3rd Qu.: 0.655
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V73 V74 V75 V76
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.873 Median :-0.855 Median :-0.642 Median :-0.547
## Mean :-0.247 Mean :-0.277 Mean :-0.282 Mean :-0.173
## 3rd Qu.: 0.836 3rd Qu.: 0.702 3rd Qu.: 0.543 3rd Qu.: 0.849
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V77 V78 V79 V80
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.478 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.144 Mean :-0.344 Mean :-0.655 Mean :-0.872
## 3rd Qu.: 0.907 3rd Qu.: 0.627 3rd Qu.:-0.756 3rd Qu.:-1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V81 V82 V83 V84
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.979 Mean :-0.971 Mean :-0.829 Mean :-0.597
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-0.583
## Max. : 0.818 Max. : 0.933 Max. : 1.000 Max. : 1.000
## V85 V86 V87 V88
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-0.667 Median :-0.702 Median :-0.746
## Mean :-0.341 Mean :-0.202 Mean :-0.232 Mean :-0.294
## 3rd Qu.: 0.648 3rd Qu.: 0.811 3rd Qu.: 0.801 3rd Qu.: 0.585
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V89 V90 V91 V92
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.00
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.00
## Median :-0.852 Median :-0.859 Median :-0.620 Median :-0.54
## Mean :-0.264 Mean :-0.264 Mean :-0.248 Mean :-0.18
## 3rd Qu.: 0.768 3rd Qu.: 0.756 3rd Qu.: 0.654 3rd Qu.: 0.87
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.00
## V93 V94 V95 V96
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.678 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.192 Mean :-0.377 Mean :-0.631 Mean :-0.846
## 3rd Qu.: 0.896 3rd Qu.: 0.507 3rd Qu.:-0.710 3rd Qu.:-1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V97 V98 V99 V100
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.970 Mean :-0.955 Mean :-0.798 Mean :-0.579
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-0.456
## Max. : 0.797 Max. : 0.891 Max. : 1.000 Max. : 1.000
## V101 V102 V103 V104
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-0.854 Median :-0.770 Median :-0.740
## Mean :-0.372 Mean :-0.238 Mean :-0.252 Mean :-0.266
## 3rd Qu.: 0.577 3rd Qu.: 0.830 3rd Qu.: 0.744 3rd Qu.: 0.661
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V105 V106 V107 V108
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.665 Median :-0.552 Median :-0.466 Median :-0.523
## Mean :-0.205 Mean :-0.153 Mean :-0.169 Mean :-0.156
## 3rd Qu.: 0.893 3rd Qu.: 0.950 3rd Qu.: 0.821 3rd Qu.: 0.926
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V109 V110 V111 V112
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.00
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.00
## Median :-0.735 Median :-1.000 Median :-1.000 Median :-1.00
## Mean :-0.226 Mean :-0.426 Mean :-0.643 Mean :-0.82
## 3rd Qu.: 0.834 3rd Qu.: 0.295 3rd Qu.:-0.754 3rd Qu.:-1.00
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.00
## V113 V114 V115 V116
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.952 Mean :-0.931 Mean :-0.773 Mean :-0.599
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-0.621
## Max. : 0.976 Max. : 0.998 Max. : 1.000 Max. : 1.000
## V117 V118 V119 V120
## Min. :-1.00 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.00 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.00 Median :-0.978 Median :-0.900 Median :-0.768
## Mean :-0.43 Mean :-0.295 Mean :-0.298 Mean :-0.286
## 3rd Qu.: 0.31 3rd Qu.: 0.768 3rd Qu.: 0.612 3rd Qu.: 0.580
## Max. : 1.00 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V121 V122 V123 V124
## Min. :-1.000 Min. :-1.0000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.0000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.672 Median :-0.3470 Median :-0.304 Median :-0.468
## Mean :-0.173 Mean :-0.0959 Mean :-0.125 Mean :-0.145
## 3rd Qu.: 0.961 3rd Qu.: 0.9965 3rd Qu.: 0.904 3rd Qu.: 0.920
## Max. : 1.000 Max. : 1.0000 Max. : 1.000 Max. : 1.000
## V125 V126 V127 V128
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.783 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.254 Mean :-0.452 Mean :-0.637 Mean :-0.793
## 3rd Qu.: 0.754 3rd Qu.: 0.189 3rd Qu.:-0.732 3rd Qu.:-1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V129 V130 V131 V132
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.923 Mean :-0.903 Mean :-0.746 Mean :-0.598
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-0.594
## Max. : 0.995 Max. : 0.995 Max. : 1.000 Max. : 1.000
## V133 V134 V135 V136
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-0.998 Median :-0.892
## Mean :-0.447 Mean :-0.363 Mean :-0.387 Mean :-0.346
## 3rd Qu.: 0.230 3rd Qu.: 0.592 3rd Qu.: 0.370 3rd Qu.: 0.468
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V137 V138 V139 V140
## Min. :-1.000 Min. :-1.0000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.0000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.728 Median :-0.2450 Median :-0.200 Median :-0.517
## Mean :-0.195 Mean :-0.0916 Mean :-0.102 Mean :-0.153
## 3rd Qu.: 0.919 3rd Qu.: 0.9545 3rd Qu.: 0.877 3rd Qu.: 0.865
## Max. : 1.000 Max. : 1.0000 Max. : 1.000 Max. : 1.000
## V141 V142 V143 V144
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.836 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.273 Mean :-0.444 Mean :-0.602 Mean :-0.755
## 3rd Qu.: 0.737 3rd Qu.: 0.260 3rd Qu.:-0.595 3rd Qu.:-1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V145 V146 V147 V148
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.900 Mean :-0.882 Mean :-0.708 Mean :-0.571
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-0.436
## Max. : 0.989 Max. : 0.989 Max. : 1.000 Max. : 1.000
## V149 V150 V151 V152
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.455 Mean :-0.436 Mean :-0.475 Mean :-0.421
## 3rd Qu.: 0.281 3rd Qu.: 0.286 3rd Qu.: 0.066 3rd Qu.: 0.225
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V153 V154 V155 V156
## Min. :-1.000 Min. :-1.0000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.0000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.856 Median :-0.2710 Median :-0.249 Median :-0.600
## Mean :-0.245 Mean :-0.0988 Mean :-0.117 Mean :-0.207
## 3rd Qu.: 0.870 3rd Qu.: 0.9365 3rd Qu.: 0.873 3rd Qu.: 0.779
## Max. : 1.000 Max. : 1.0000 Max. : 1.000 Max. : 1.000
## V157 V158 V159 V160
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.890 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.288 Mean :-0.417 Mean :-0.552 Mean :-0.722
## 3rd Qu.: 0.691 3rd Qu.: 0.352 3rd Qu.:-0.297 3rd Qu.:-1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V161 V162 V163 V164
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.896 Mean :-0.868 Mean :-0.675 Mean :-0.542
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-0.275
## Max. : 0.996 Max. : 0.990 Max. : 1.000 Max. : 1.000
## V165 V166 V167 V168
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.0000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.0000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.0000
## Mean :-0.458 Mean :-0.464 Mean :-0.532 Mean :-0.4885
## 3rd Qu.: 0.202 3rd Qu.: 0.155 3rd Qu.:-0.174 3rd Qu.:-0.0045
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.0000
## V169 V170 V171 V172
## Min. :-1.000 Min. :-1.0000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.0000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.943 Median :-0.3040 Median :-0.418 Median :-0.682
## Mean :-0.269 Mean :-0.0963 Mean :-0.148 Mean :-0.260
## 3rd Qu.: 0.801 3rd Qu.: 0.9515 3rd Qu.: 0.881 3rd Qu.: 0.661
## Max. : 1.000 Max. : 1.0000 Max. : 1.000 Max. : 1.000
## V173 V174 V175 V176
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.931 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.306 Mean :-0.365 Mean :-0.521 Mean :-0.708
## 3rd Qu.: 0.667 3rd Qu.: 0.532 3rd Qu.:-0.153 3rd Qu.:-1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V177 V178 V179 V180
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.0000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.0000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.0000
## Mean :-0.910 Mean :-0.891 Mean :-0.677 Mean :-0.5018
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-0.984 3rd Qu.:-0.0415
## Max. : 0.938 Max. : 0.909 Max. : 1.000 Max. : 1.0000
## V181 V182 V183 V184
## Min. :-1.000 Min. :-1.000 Min. :-1.0000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.0000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.0000 Median :-1.000
## Mean :-0.384 Mean :-0.414 Mean :-0.5115 Mean :-0.484
## 3rd Qu.: 0.504 3rd Qu.: 0.392 3rd Qu.:-0.0935 3rd Qu.: 0.002
## Max. : 1.000 Max. : 1.000 Max. : 1.0000 Max. : 1.000
## V185 V186 V187 V188
## Min. :-1.000 Min. :-1.0000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.0000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.793 Median :-0.1520 Median :-0.384 Median :-0.723
## Mean :-0.249 Mean :-0.0704 Mean :-0.164 Mean :-0.249
## 3rd Qu.: 0.806 3rd Qu.: 0.9725 3rd Qu.: 0.807 3rd Qu.: 0.696
## Max. : 1.000 Max. : 1.0000 Max. : 1.000 Max. : 1.000
## V189 V190 V191 V192
## Min. :-1.000 Min. :-1.000 Min. :-1.0000 Min. :-1.00
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.0000 1st Qu.:-1.00
## Median :-0.872 Median :-1.000 Median :-1.0000 Median :-1.00
## Mean :-0.253 Mean :-0.327 Mean :-0.5185 Mean :-0.74
## 3rd Qu.: 0.806 3rd Qu.: 0.711 3rd Qu.:-0.0835 3rd Qu.:-1.00
## Max. : 1.000 Max. : 1.000 Max. : 1.0000 Max. : 1.00
## V193 V194 V195 V196
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.925 Mean :-0.925 Mean :-0.728 Mean :-0.487
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-0.992 3rd Qu.: 0.144
## Max. : 0.986 Max. : 0.955 Max. : 1.000 Max. : 1.000
## V197 V198 V199 V200
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-0.963 Median :-0.990 Median :-0.853
## Mean :-0.299 Mean :-0.283 Mean :-0.386 Mean :-0.353
## 3rd Qu.: 0.765 3rd Qu.: 0.788 3rd Qu.: 0.420 3rd Qu.: 0.371
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V201 V202 V203 V204
## Min. :-1.000 Min. :-1.0000 Min. :-1.0000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.0000 1st Qu.:-0.9880 1st Qu.:-1.000
## Median :-0.327 Median : 0.2360 Median :-0.1160 Median :-0.465
## Mean :-0.107 Mean : 0.0467 Mean :-0.0621 Mean :-0.141
## 3rd Qu.: 0.962 3rd Qu.: 0.9970 3rd Qu.: 0.8660 3rd Qu.: 0.925
## Max. : 1.000 Max. : 1.0000 Max. : 1.0000 Max. : 1.000
## V205 V206 V207 V208
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-0.684 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.168 Mean :-0.325 Mean :-0.576 Mean :-0.809
## 3rd Qu.: 0.934 3rd Qu.: 0.684 3rd Qu.:-0.376 3rd Qu.:-1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V209 V210 V211 V212
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.943 Mean :-0.960 Mean :-0.839 Mean :-0.614
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-0.561
## Max. : 0.887 Max. : 0.834 Max. : 1.000 Max. : 1.000
## V213 V214 V215 V216
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.0000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-0.9945
## Median :-1.000 Median :-0.533 Median :-0.416 Median :-0.0880
## Mean :-0.333 Mean :-0.143 Mean :-0.133 Mean :-0.0427
## 3rd Qu.: 0.652 3rd Qu.: 0.947 3rd Qu.: 0.939 3rd Qu.: 0.8790
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.0000
## V217 V218 V219 V220
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-0.689 1st Qu.:-0.374 1st Qu.:-0.775 1st Qu.:-1.000
## Median : 0.572 Median : 0.700 Median : 0.306 Median :-0.043
## Mean : 0.225 Mean : 0.319 Mean : 0.136 Mean :-0.026
## 3rd Qu.: 1.000 3rd Qu.: 1.000 3rd Qu.: 1.000 3rd Qu.: 1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V221 V222 V223 V224
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.00
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.00
## Median :-0.749 Median :-1.000 Median :-1.000 Median :-1.00
## Mean :-0.186 Mean :-0.450 Mean :-0.715 Mean :-0.88
## 3rd Qu.: 0.930 3rd Qu.: 0.258 3rd Qu.:-0.936 3rd Qu.:-1.00
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.00
## V225 V226 V227 V228
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.966 Mean :-0.982 Mean :-0.934 Mean :-0.819
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000
## Max. : 0.720 Max. : 0.887 Max. : 1.000 Max. : 1.000
## V229 V230 V231 V232
## Min. :-1.000 Min. :-1.000 Min. :-1.0000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.0000 1st Qu.:-0.806
## Median :-1.000 Median :-0.934 Median :-0.0350 Median : 0.583
## Mean :-0.578 Mean :-0.266 Mean :-0.0317 Mean : 0.203
## 3rd Qu.:-0.349 3rd Qu.: 0.732 3rd Qu.: 1.0000 3rd Qu.: 1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.0000 Max. : 1.000
## V233 V234 V235 V236
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.: 0.186 1st Qu.: 0.182 1st Qu.:-0.853 1st Qu.:-1.000
## Median : 0.974 Median : 0.969 Median : 0.525 Median :-0.552
## Mean : 0.507 Mean : 0.501 Mean : 0.178 Mean :-0.149
## 3rd Qu.: 1.000 3rd Qu.: 1.000 3rd Qu.: 1.000 3rd Qu.: 0.913
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V237 V238 V239 V240
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.454 Mean :-0.707 Mean :-0.863 Mean :-0.942
## 3rd Qu.: 0.158 3rd Qu.:-0.898 3rd Qu.:-1.000 3rd Qu.:-1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V241 V242 V243 V244
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.984 Mean :-0.998 Mean :-0.985 Mean :-0.955
## 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000
## Max. : 0.677 Max. : 0.501 Max. : 0.773 Max. : 1.000
## V245 V246 V247 V248
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-0.987 Median :-0.337
## Mean :-0.883 Mean :-0.729 Mean :-0.472 Mean :-0.171
## 3rd Qu.:-1.000 3rd Qu.:-0.745 3rd Qu.: 0.098 3rd Qu.: 0.641
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V249 V250 V251 V252
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-0.684 1st Qu.:-0.746 1st Qu.:-1.000 1st Qu.:-1.000
## Median : 0.333 Median : 0.333 Median :-0.577 Median :-1.000
## Mean : 0.134 Mean : 0.110 Mean :-0.315 Mean :-0.636
## 3rd Qu.: 0.853 3rd Qu.: 0.805 3rd Qu.: 0.335 3rd Qu.:-0.435
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 1.000
## V253 V254 V255 V256
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000 1st Qu.:-1.000
## Median :-1.000 Median :-1.000 Median :-1.000 Median :-1.000
## Mean :-0.818 Mean :-0.913 Mean :-0.961 Mean :-0.984
## 3rd Qu.:-0.999 3rd Qu.:-1.000 3rd Qu.:-1.000 3rd Qu.:-1.000
## Max. : 1.000 Max. : 1.000 Max. : 1.000 Max. : 0.992
## V257
## Min. :-1.000
## 1st Qu.:-1.000
## Median :-1.000
## Mean :-0.996
## 3rd Qu.:-1.000
## Max. : 0.563
sapply(DATASET.test[1, ], class)
## V1 V2 V3 V4 V5 V6 V7
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V8 V9 V10 V11 V12 V13 V14
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V15 V16 V17 V18 V19 V20 V21
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V22 V23 V24 V25 V26 V27 V28
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V29 V30 V31 V32 V33 V34 V35
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V36 V37 V38 V39 V40 V41 V42
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V43 V44 V45 V46 V47 V48 V49
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V50 V51 V52 V53 V54 V55 V56
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V57 V58 V59 V60 V61 V62 V63
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V64 V65 V66 V67 V68 V69 V70
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V71 V72 V73 V74 V75 V76 V77
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V78 V79 V80 V81 V82 V83 V84
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V85 V86 V87 V88 V89 V90 V91
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V92 V93 V94 V95 V96 V97 V98
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V99 V100 V101 V102 V103 V104 V105
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V106 V107 V108 V109 V110 V111 V112
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V113 V114 V115 V116 V117 V118 V119
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V120 V121 V122 V123 V124 V125 V126
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V127 V128 V129 V130 V131 V132 V133
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V134 V135 V136 V137 V138 V139 V140
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V141 V142 V143 V144 V145 V146 V147
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V148 V149 V150 V151 V152 V153 V154
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V155 V156 V157 V158 V159 V160 V161
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V162 V163 V164 V165 V166 V167 V168
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V169 V170 V171 V172 V173 V174 V175
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V176 V177 V178 V179 V180 V181 V182
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V183 V184 V185 V186 V187 V188 V189
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V190 V191 V192 V193 V194 V195 V196
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V197 V198 V199 V200 V201 V202 V203
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V204 V205 V206 V207 V208 V209 V210
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V211 V212 V213 V214 V215 V216 V217
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V218 V219 V220 V221 V222 V223 V224
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V225 V226 V227 V228 V229 V230 V231
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V232 V233 V234 V235 V236 V237 V238
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V239 V240 V241 V242 V243 V244 V245
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V246 V247 V248 V249 V250 V251 V252
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## V253 V254 V255 V256 V257
## "numeric" "numeric" "numeric" "numeric" "numeric"
sum(is.na(DATASET.train))
## [1] 0
sum(is.na(DATASET.test))
## [1] 0
DATASET.train[, 1] <- as.factor(DATASET.train[, 1]) # As Category
colnames(DATASET.train) <- c("Y", paste("X.", 1:256, sep = ""))
class(DATASET.train[, 1])
## [1] "factor"
levels(DATASET.train[, 1])
## [1] "0" "1" "2" "3" "4" "5" "6" "7" "8" "9"
sapply(DATASET.train[1, ], class)
## Y X.1 X.2 X.3 X.4 X.5 X.6
## "factor" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.7 X.8 X.9 X.10 X.11 X.12 X.13
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.14 X.15 X.16 X.17 X.18 X.19 X.20
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.21 X.22 X.23 X.24 X.25 X.26 X.27
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.28 X.29 X.30 X.31 X.32 X.33 X.34
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.35 X.36 X.37 X.38 X.39 X.40 X.41
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.42 X.43 X.44 X.45 X.46 X.47 X.48
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.49 X.50 X.51 X.52 X.53 X.54 X.55
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.56 X.57 X.58 X.59 X.60 X.61 X.62
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.63 X.64 X.65 X.66 X.67 X.68 X.69
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.70 X.71 X.72 X.73 X.74 X.75 X.76
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.77 X.78 X.79 X.80 X.81 X.82 X.83
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.84 X.85 X.86 X.87 X.88 X.89 X.90
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.91 X.92 X.93 X.94 X.95 X.96 X.97
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.98 X.99 X.100 X.101 X.102 X.103 X.104
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.105 X.106 X.107 X.108 X.109 X.110 X.111
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.112 X.113 X.114 X.115 X.116 X.117 X.118
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.119 X.120 X.121 X.122 X.123 X.124 X.125
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.126 X.127 X.128 X.129 X.130 X.131 X.132
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.133 X.134 X.135 X.136 X.137 X.138 X.139
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.140 X.141 X.142 X.143 X.144 X.145 X.146
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.147 X.148 X.149 X.150 X.151 X.152 X.153
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.154 X.155 X.156 X.157 X.158 X.159 X.160
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.161 X.162 X.163 X.164 X.165 X.166 X.167
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.168 X.169 X.170 X.171 X.172 X.173 X.174
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.175 X.176 X.177 X.178 X.179 X.180 X.181
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.182 X.183 X.184 X.185 X.186 X.187 X.188
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.189 X.190 X.191 X.192 X.193 X.194 X.195
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.196 X.197 X.198 X.199 X.200 X.201 X.202
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.203 X.204 X.205 X.206 X.207 X.208 X.209
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.210 X.211 X.212 X.213 X.214 X.215 X.216
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.217 X.218 X.219 X.220 X.221 X.222 X.223
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.224 X.225 X.226 X.227 X.228 X.229 X.230
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.231 X.232 X.233 X.234 X.235 X.236 X.237
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.238 X.239 X.240 X.241 X.242 X.243 X.244
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.245 X.246 X.247 X.248 X.249 X.250 X.251
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.252 X.253 X.254 X.255 X.256
## "numeric" "numeric" "numeric" "numeric" "numeric"
DATASET.test[, 1] <- as.factor(DATASET.test[, 1]) # As Category
colnames(DATASET.test) <- c("Y", paste("X.", 1:256, sep = ""))
class(DATASET.test[, 1])
## [1] "factor"
levels(DATASET.test[, 1])
## [1] "0" "1" "2" "3" "4" "5" "6" "7" "8" "9"
sapply(DATASET.test[1, ], class)
## Y X.1 X.2 X.3 X.4 X.5 X.6
## "factor" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.7 X.8 X.9 X.10 X.11 X.12 X.13
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.14 X.15 X.16 X.17 X.18 X.19 X.20
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.21 X.22 X.23 X.24 X.25 X.26 X.27
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.28 X.29 X.30 X.31 X.32 X.33 X.34
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.35 X.36 X.37 X.38 X.39 X.40 X.41
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.42 X.43 X.44 X.45 X.46 X.47 X.48
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.49 X.50 X.51 X.52 X.53 X.54 X.55
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.56 X.57 X.58 X.59 X.60 X.61 X.62
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.63 X.64 X.65 X.66 X.67 X.68 X.69
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.70 X.71 X.72 X.73 X.74 X.75 X.76
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.77 X.78 X.79 X.80 X.81 X.82 X.83
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.84 X.85 X.86 X.87 X.88 X.89 X.90
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.91 X.92 X.93 X.94 X.95 X.96 X.97
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.98 X.99 X.100 X.101 X.102 X.103 X.104
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.105 X.106 X.107 X.108 X.109 X.110 X.111
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.112 X.113 X.114 X.115 X.116 X.117 X.118
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.119 X.120 X.121 X.122 X.123 X.124 X.125
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.126 X.127 X.128 X.129 X.130 X.131 X.132
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.133 X.134 X.135 X.136 X.137 X.138 X.139
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.140 X.141 X.142 X.143 X.144 X.145 X.146
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.147 X.148 X.149 X.150 X.151 X.152 X.153
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.154 X.155 X.156 X.157 X.158 X.159 X.160
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.161 X.162 X.163 X.164 X.165 X.166 X.167
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.168 X.169 X.170 X.171 X.172 X.173 X.174
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.175 X.176 X.177 X.178 X.179 X.180 X.181
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.182 X.183 X.184 X.185 X.186 X.187 X.188
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.189 X.190 X.191 X.192 X.193 X.194 X.195
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.196 X.197 X.198 X.199 X.200 X.201 X.202
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.203 X.204 X.205 X.206 X.207 X.208 X.209
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.210 X.211 X.212 X.213 X.214 X.215 X.216
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.217 X.218 X.219 X.220 X.221 X.222 X.223
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.224 X.225 X.226 X.227 X.228 X.229 X.230
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.231 X.232 X.233 X.234 X.235 X.236 X.237
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.238 X.239 X.240 X.241 X.242 X.243 X.244
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.245 X.246 X.247 X.248 X.249 X.250 X.251
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## X.252 X.253 X.254 X.255 X.256
## "numeric" "numeric" "numeric" "numeric" "numeric"
# write.arff(DATASET.train, paste0(DATASET_DIR,'zip.train.arff'))
# write.arff(DATASET.test, paste0(DATASET_DIR,'zip.test.arff'))
More info http://www.r-bloggers.com/the-essence-of-a-handwritten-digit/
par(mfrow = c(4, 3), pty = "s", mar = c(1, 1, 1, 1), xaxt = "n", yaxt = "n")
images_digits_0_9 <- array(dim = c(10, 16 * 16))
for (digit in 0:9) {
print(digit)
images_digits_0_9[digit + 1, ] <- apply(DATASET.train[DATASET.train[, 1] ==
digit, -1], 2, sum)
images_digits_0_9[digit + 1, ] <- images_digits_0_9[digit + 1, ]/max(images_digits_0_9[digit +
1, ]) * 255
z <- array(images_digits_0_9[digit + 1, ], dim = c(16, 16))
z <- z[, 16:1] ##right side up
image(1:16, 1:16, z, main = digit, col = CUSTOM_COLORS(256))
}
## [1] 0
## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
# pdf(file=paste0(FIGURES_DIR,'trainDigit.pdf'),)
par(mfrow = c(4, 4), pty = "s", mar = c(3, 3, 3, 3), xaxt = "n", yaxt = "n")
for (i in 1:10) {
z <- array(as.vector(as.matrix(DATASET.train[i, -1])), dim = c(16, 16))
z <- z[, 16:1] ##right side up
image(1:16, 1:16, z, main = DATASET.train[i, 1], col = CUSTOM_COLORS(256))
print(i)
}
## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
## [1] 10
resTable <- table(DATASET.train$Y)
par(mfrow = c(1, 1))
par(mar = c(5, 4, 4, 2) + 0.1) # increase y-axis margin.
plot <- plot(DATASET.train$Y, col = CUSTOM_COLORS_PLOT(10), main = "Total Number of Digits (Training Set)",
ylim = c(0, 1500), ylab = "Examples Number")
text(x = plot, y = resTable + 50, labels = resTable, cex = 0.75)
par(mfrow = c(1, 1))
percentage <- round(resTable/sum(resTable) * 100)
labels <- paste0(row.names(resTable), " (", percentage, "%) ") # add percents to labels
pie(resTable, labels = labels, col = CUSTOM_COLORS_PLOT(10), main = "Total Number of Digits (Training Set)")
resTable <- table(DATASET.test$Y)
par(mfrow = c(1, 1))
par(mar = c(5, 4, 4, 2) + 0.1) # increase y-axis margin.
plot <- plot(DATASET.test$Y, col = CUSTOM_COLORS_PLOT(10), main = "Total Number of Digits (Testing Set)",
ylim = c(0, 400), ylab = "Examples Number")
text(x = plot, y = resTable + 20, labels = resTable, cex = 0.75)
par(mfrow = c(1, 1))
percentage <- round(resTable/sum(resTable) * 100)
labels <- paste0(row.names(resTable), " (", percentage, "%) ") # add percents to labels
pie(resTable, labels = labels, col = CUSTOM_COLORS_PLOT(10), main = "Total Number of Digits (Testing Set)")
pc <- proc.time()
model.rpart <- rpart(DATASET.train$Y ~ ., method = "class", data = DATASET.train)
proc.time() - pc
## user system elapsed
## 16.14 0.16 16.38
printcp(model.rpart)
##
## Classification tree:
## rpart(formula = DATASET.train$Y ~ ., data = DATASET.train, method = "class")
##
## Variables actually used in tree construction:
## [1] X.120 X.121 X.167 X.169 X.198 X.213 X.24 X.246 X.248 X.72 X.76
## [12] X.88
##
## Root node error: 6097/7291 = 0.84
##
## n= 7291
##
## CP nsplit rel error xerror xstd
## 1 0.159 0 1.00 1.00 0.0052
## 2 0.087 1 0.84 0.84 0.0064
## 3 0.078 2 0.75 0.76 0.0067
## 4 0.075 3 0.68 0.69 0.0069
## 5 0.065 4 0.60 0.60 0.0070
## 6 0.064 5 0.54 0.51 0.0069
## 7 0.047 6 0.47 0.47 0.0069
## 8 0.042 7 0.42 0.43 0.0067
## 9 0.040 8 0.38 0.40 0.0066
## 10 0.031 9 0.34 0.35 0.0064
## 11 0.013 10 0.31 0.32 0.0062
## 12 0.011 11 0.30 0.31 0.0062
## 13 0.011 12 0.29 0.31 0.0061
## 14 0.010 13 0.28 0.31 0.0061
plot(model.rpart, uniform = TRUE, main = "Classification (RPART). Tree of Handwritten Digit Recognition ")
text(model.rpart, all = TRUE, cex = 0.75)
draw.tree(model.rpart, cex = 0.5, nodeinfo = TRUE, col = gray(0:8/8))
prediction.rpart <- predict(model.rpart, newdata = DATASET.test, type = "class")
table(`Actual Class` = DATASET.test$Y, `Predicted Class` = prediction.rpart)
## Predicted Class
## Actual Class 0 1 2 3 4 5 6 7 8 9
## 0 294 3 31 12 1 2 1 0 15 0
## 1 1 243 2 0 3 1 0 1 5 8
## 2 9 9 111 10 13 2 11 5 24 4
## 3 10 4 2 105 3 22 0 3 16 1
## 4 1 26 11 0 138 1 0 5 5 13
## 5 17 11 6 15 2 89 10 2 7 1
## 6 11 8 4 1 2 10 104 3 27 0
## 7 0 9 6 1 10 1 0 113 3 4
## 8 1 9 11 12 2 3 1 2 120 5
## 9 0 16 2 5 1 0 0 8 7 138
error.rate.rpart <- sum(DATASET.test$Y != prediction.rpart)/nrow(DATASET.test)
print(paste0("Accuary (Precision): ", 1 - error.rate.rpart))
## [1] "Accuary (Precision): 0.724962630792227"
row <- 1
prediction.digit <- as.vector(predict(model.rpart, newdata = DATASET.test[row,
], type = "class"))
print(paste0("Current Digit: ", as.character(DATASET.test$Y[row])))
## [1] "Current Digit: 9"
print(paste0("Predicted Digit: ", prediction.digit))
## [1] "Predicted Digit: 9"
z <- array(as.vector(as.matrix(DATASET.test[row, -1])), dim = c(16, 16))
z <- z[, 16:1] ##right side up
par(mfrow = c(1, 3), pty = "s", mar = c(1, 1, 1, 1), xaxt = "n", yaxt = "n")
image(1:16, 1:16, z, main = DATASET.test[row, 1], col = CUSTOM_COLORS(256))
pc <- proc.time()
model.knn <- IBk(DATASET.train$Y ~ ., data = DATASET.train)
proc.time() - pc
## user system elapsed
## 77.56 0.30 77.79
summary(model.knn)
##
## === Summary ===
##
## Correctly Classified Instances 7291 100 %
## Incorrectly Classified Instances 0 0 %
## Kappa statistic 1
## Mean absolute error 0.0002
## Root mean squared error 0.0004
## Relative absolute error 0.1381 %
## Root relative squared error 0.1375 %
## Coverage of cases (0.95 level) 100 %
## Mean rel. region size (0.95 level) 10 %
## Total Number of Instances 7291
##
## === Confusion Matrix ===
##
## a b c d e f g h i j <-- classified as
## 1194 0 0 0 0 0 0 0 0 0 | a = 0
## 0 1005 0 0 0 0 0 0 0 0 | b = 1
## 0 0 731 0 0 0 0 0 0 0 | c = 2
## 0 0 0 658 0 0 0 0 0 0 | d = 3
## 0 0 0 0 652 0 0 0 0 0 | e = 4
## 0 0 0 0 0 556 0 0 0 0 | f = 5
## 0 0 0 0 0 0 664 0 0 0 | g = 6
## 0 0 0 0 0 0 0 645 0 0 | h = 7
## 0 0 0 0 0 0 0 0 542 0 | i = 8
## 0 0 0 0 0 0 0 0 0 644 | j = 9
prediction.knn <- predict(model.knn, newdata = DATASET.test, type = "class")
table(`Actual Class` = DATASET.test$Y, `Predicted Class` = prediction.knn)
## Predicted Class
## Actual Class 0 1 2 3 4 5 6 7 8 9
## 0 355 0 2 0 0 0 0 1 0 1
## 1 0 255 0 0 6 0 2 1 0 0
## 2 6 1 183 2 1 0 0 2 3 0
## 3 3 0 2 154 0 5 0 0 0 2
## 4 0 3 1 0 182 1 2 2 1 8
## 5 2 1 2 4 0 145 2 0 3 1
## 6 0 0 1 0 2 3 164 0 0 0
## 7 0 1 1 1 4 0 0 139 0 1
## 8 5 0 1 6 1 1 0 1 148 3
## 9 0 0 1 0 2 0 0 4 1 169
error.rate.knn <- sum(DATASET.test$Y != prediction.knn)/nrow(DATASET.test)
print(paste0("Accuary (Precision): ", 1 - error.rate.knn))
## [1] "Accuary (Precision): 0.943697060288989"
row <- 1
prediction.digit <- as.vector(predict(model.knn, newdata = DATASET.test[row,
], type = "class"))
print(paste0("Current Digit: ", as.character(DATASET.test$Y[row])))
## [1] "Current Digit: 9"
print(paste0("Predicted Digit: ", prediction.digit))
## [1] "Predicted Digit: 9"
par(mfrow = c(1, 3), pty = "s", mar = c(1, 1, 1, 1), xaxt = "n", yaxt = "n")
z <- array(as.vector(as.matrix(DATASET.test[row, -1])), dim = c(16, 16))
z <- z[, 16:1] ##right side up
image(1:16, 1:16, z, main = DATASET.test[row, 1], col = CUSTOM_COLORS(256))
pc <- proc.time()
# Avoid Name Collision (knn)
model.fnn <- FNN::knn(DATASET.train[, -1], DATASET.test[, -1], DATASET.train$Y,
k = 10, algorithm = "cover_tree")
proc.time() - pc
## user system elapsed
## 18.753 0.024 18.783
summary(model.fnn)
## 0 1 2 3 4 5 6 7 8 9
## 381 270 190 166 193 154 167 148 151 187
table(`Actual Class` = DATASET.test$Y, `Predicted Class` = model.fnn)
## Predicted Class
## Actual Class 0 1 2 3 4 5 6 7 8 9
## 0 354 0 2 0 1 0 1 0 0 1
## 1 0 258 0 0 3 0 3 0 0 0
## 2 7 2 180 2 1 0 0 2 4 0
## 3 3 0 2 153 0 6 0 1 0 1
## 4 0 4 2 0 181 0 2 2 0 9
## 5 5 0 1 5 0 143 0 0 1 5
## 6 4 0 2 0 2 1 160 0 1 0
## 7 0 3 1 0 4 1 0 137 0 1
## 8 7 3 0 6 0 2 1 1 144 2
## 9 1 0 0 0 1 1 0 5 1 168
error.rate.fnn <- sum(DATASET.test$Y != model.fnn)/nrow(DATASET.test)
print(paste0("Accuary (Precision): ", 1 - error.rate.fnn))
## [1] "Accuary (Precision): 0.935724962630792"
row <- 1
prediction.digit <- model.fnn[row]
print(paste0("Current Digit: ", as.character(DATASET.test$Y[row])))
## [1] "Current Digit: 9"
print(paste0("Predicted Digit: ", prediction.digit))
## [1] "Predicted Digit: 9"
par(mfrow = c(1, 3), pty = "s", mar = c(1, 1, 1, 1), xaxt = "n", yaxt = "n")
z <- array(as.vector(as.matrix(DATASET.test[row, -1])), dim = c(16, 16))
z <- z[, 16:1] ##right side up
image(1:16, 1:16, z, main = DATASET.test[row, 1], col = CUSTOM_COLORS(256))
pc <- proc.time()
model.naiveBayes <- naiveBayes(DATASET.train$Y ~ ., data = DATASET.train)
proc.time() - pc
## user system elapsed
## 2.692 0.008 2.703
summary(model.naiveBayes)
## Length Class Mode
## apriori 10 table numeric
## tables 256 -none- list
## levels 10 -none- character
## call 4 -none- call
prediction.naiveBayes <- predict(model.naiveBayes, newdata = DATASET.test, type = "class")
table(`Actual Class` = DATASET.test$Y, `Predicted Class` = prediction.naiveBayes)
## Predicted Class
## Actual Class 0 1 2 3 4 5 6 7 8 9
## 0 298 1 12 0 4 2 11 1 29 1
## 1 0 253 1 0 1 0 5 0 1 3
## 2 3 0 142 5 3 3 12 1 28 1
## 3 6 0 10 90 1 4 4 1 44 6
## 4 0 2 5 0 66 1 9 2 6 109
## 5 9 1 1 7 2 78 38 1 18 5
## 6 3 1 5 0 0 2 156 0 3 0
## 7 0 1 1 0 4 0 0 127 1 13
## 8 0 1 5 1 2 7 2 0 120 28
## 9 0 10 0 0 3 0 0 7 5 152
error.rate.naiveBayes <- sum(DATASET.test$Y != prediction.naiveBayes)/nrow(DATASET.test)
print(paste0("Accuary (Precision): ", 1 - error.rate.naiveBayes))
## [1] "Accuary (Precision): 0.738415545590434"
# All Prediction for Row 1
row <- 1
prediction.digit <- as.vector(predict(model.naiveBayes, newdata = DATASET.test[row,
], type = "class"))
print(paste0("Current Digit: ", as.character(DATASET.test$Y[row])))
## [1] "Current Digit: 9"
print(paste0("Predicted Digit: ", prediction.digit))
## [1] "Predicted Digit: 9"
z <- array(as.vector(as.matrix(DATASET.test[row, -1])), dim = c(16, 16))
z <- z[, 16:1] ##right side up
par(mfrow = c(1, 3), pty = "s", mar = c(1, 1, 1, 1), xaxt = "n", yaxt = "n")
image(1:16, 1:16, z, main = DATASET.test[row, 1], col = CUSTOM_COLORS(256))
pc <- proc.time()
model.svm <- svm(DATASET.train$Y ~ ., method = "class", data = DATASET.train)
proc.time() - pc
## user system elapsed
## 69.488 0.424 73.056
summary(model.svm)
##
## Call:
## svm(formula = DATASET.train$Y ~ ., data = DATASET.train, method = "class")
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: radial
## cost: 1
## gamma: 0.003906
##
## Number of Support Vectors: 2606
##
## ( 213 326 319 235 285 63 256 262 401 246 )
##
##
## Number of Classes: 10
##
## Levels:
## 0 1 2 3 4 5 6 7 8 9
# plot(model.svm, DATASET.train, DATASET.train$Y ~z )
prediction.svm <- predict(model.svm, newdata = DATASET.test, type = "class")
table(`Actual Class` = DATASET.test$Y, `Predicted Class` = prediction.svm)
## Predicted Class
## Actual Class 0 1 2 3 4 5 6 7 8 9
## 0 351 0 6 0 1 0 0 0 1 0
## 1 0 253 1 0 5 1 3 1 0 0
## 2 2 0 183 4 3 0 1 1 4 0
## 3 0 0 5 146 0 11 0 1 3 0
## 4 0 1 3 0 186 1 2 3 1 3
## 5 3 0 2 3 1 147 0 0 1 3
## 6 4 0 4 0 2 1 158 0 1 0
## 7 0 0 2 0 5 0 0 138 0 2
## 8 4 0 2 3 0 2 1 0 151 3
## 9 0 0 0 0 4 1 0 0 2 170
error.rate.svm <- sum(DATASET.test$Y != prediction.svm)/nrow(DATASET.test)
print(paste0("Accuary (Precision): ", 1 - error.rate.svm))
## [1] "Accuary (Precision): 0.938216243148979"
# All Prediction for Row 1
row <- 1
prediction.digit <- as.vector(predict(model.svm, newdata = DATASET.test[row,
], type = "class"))
print(paste0("Current Digit: ", as.character(DATASET.test$Y[row])))
## [1] "Current Digit: 9"
print(paste0("Predicted Digit: ", prediction.digit))
## [1] "Predicted Digit: 9"
z <- array(as.vector(as.matrix(DATASET.test[row, -1])), dim = c(16, 16))
z <- z[, 16:1] ##right side up
par(mfrow = c(1, 3), pty = "s", mar = c(1, 1, 1, 1), xaxt = "n", yaxt = "n")
image(1:16, 1:16, z, main = DATASET.test[row, 1], col = CUSTOM_COLORS(256))
errors <- as.vector(which(DATASET.test$Y != prediction.svm))
print(paste0("Error Numbers: ", length(errors)))
## [1] "Error Numbers: 124"
predicted <- as.vector(prediction.svm)
par(mfrow = c(4, 4), pty = "s", mar = c(3, 3, 3, 3), xaxt = "n", yaxt = "n")
for (i in 1:length(errors)) {
z <- array(as.vector(as.matrix(DATASET.test[errors[i], -1])), dim = c(16,
16))
z <- z[, 16:1] ##right side up
image(1:16, 1:16, z, main = paste0("C.D.:", as.character(DATASET.test$Y[i]),
" - Pr.D.:", predicted[errors[i]]), col = CUSTOM_COLORS(256))
}
