Navie Bayes Classifier on MNIST
Janpu Hou
September 5, 2017
STEP 1: Data Acquisition and Cleanup
Load MNIST Datasets (Train and Test)
Load the MNIST digit recognition dataset into R http://yann.lecun.com/exdb/mnist/. # Python can covert them into csv https://pjreddie.com/projects/mnist-in-csv/.
The naive bayes classifier to train model and predict the images.
library(e1071) ##deals with Probability group theory functions
library(RColorBrewer) ##coloring of graphs
library(caret) ##classification and regression technique## Loading required package: lattice## Loading required package: ggplot2train=read.csv("D:/R_Files/MNIST_data/mnist_train.csv")
test=read.csv("D:/R_Files/MNIST_data/mnist_test.csv")
DATASET.train=as.data.frame(train)
DATASET.test= as.data.frame(test)Check Training Set
Descriptive Analysis of Training dataset. here we would like to see the dimention of the dataframe of training set. how the differnt numbers from 0-9 as target variables are present. we will check their percentage of distribution through out the dataframe.
head(DATASET.train)## shows heading## X5 X0 X0.1 X0.2 X0.3 X0.4 X0.5 X0.6 X0.7 X0.8 X0.9 X0.10 X0.11 X0.12
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 4 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 9 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## X0.13 X0.14 X0.15 X0.16 X0.17 X0.18 X0.19 X0.20 X0.21 X0.22 X0.23 X0.24
## 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0
## X0.25 X0.26 X0.27 X0.28 X0.29 X0.30 X0.31 X0.32 X0.33 X0.34 X0.35 X0.36
## 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0
## X0.37 X0.38 X0.39 X0.40 X0.41 X0.42 X0.43 X0.44 X0.45 X0.46 X0.47 X0.48
## 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0
## X0.49 X0.50 X0.51 X0.52 X0.53 X0.54 X0.55 X0.56 X0.57 X0.58 X0.59 X0.60
## 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0
## X0.61 X0.62 X0.63 X0.64 X0.65 X0.66 X0.67 X0.68 X0.69 X0.70 X0.71 X0.72
## 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0
## X0.73 X0.74 X0.75 X0.76 X0.77 X0.78 X0.79 X0.80 X0.81 X0.82 X0.83 X0.84
## 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0
## X0.85 X0.86 X0.87 X0.88 X0.89 X0.90 X0.91 X0.92 X0.93 X0.94 X0.95 X0.96
## 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0
## X0.97 X0.98 X0.99 X0.100 X0.101 X0.102 X0.103 X0.104 X0.105 X0.106
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.107 X0.108 X0.109 X0.110 X0.111 X0.112 X0.113 X0.114 X0.115 X0.116
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
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## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 145 255 211
## X0.127 X0.128 X0.129 X0.130 X0.131 X0.132 X0.133 X0.134 X0.135 X0.136
## 1 51 159 253 159 50 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
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## 4 0 0 0 0 0 0 0 0 0 0
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## 6 31 0 0 0 0 0 0 0 0 0
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## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
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## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.147 X0.148 X0.149 X0.150 X0.151 X3 X18 X18.1 X18.2 X126 X136 X175
## 1 0 0 0 0 0 0 0 48 238 252 252 252
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 124
## 4 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 13 25 100 122
## 6 0 0 0 0 32 237 253 252 71 0 0 0
## X26 X166 X255 X247 X127 X0.152 X0.153 X0.154 X0.155 X0.156 X0.157 X0.158
## 1 237 0 0 0 0 0 0 0 0 0 0 0
## 2 0 67 232 39 0 0 0 0 0 0 0 0
## 3 253 255 63 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0
## 5 7 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0
## X0.159 X0.160 X0.161 X0.162 X0.163 X30 X36 X94 X154 X170 X253 X253.1
## 1 0 0 0 0 0 0 0 0 0 0 54 227
## 2 0 62 81 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 33 151
## 6 0 0 0 0 0 0 0 0 11 175 253 252
## X253.2 X253.3 X253.4 X225 X172 X253.5 X242 X195 X64 X0.164 X0.165 X0.166
## 1 253 252 239 233 252 57 6 0 0 0 0 0
## 2 0 0 0 0 0 120 180 39 0 0 0 0
## 3 0 0 96 244 251 253 62 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0
## 5 208 252 252 252 146 0 0 0 0 0 0 0
## 6 71 0 0 0 0 0 0 0 0 0 0 0
## X0.167 X0.168 X0.169 X0.170 X0.171 X0.172 X0.173 X0.174 X49 X238 X253.6
## 1 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 126 163 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0
## X253.7 X253.8 X253.9 X253.10 X253.11 X253.12 X253.13 X251 X93 X82 X82.1
## 1 0 10 60 224 252 253 252 202 84 252 253
## 2 0 0 0 0 0 0 0 0 0 2 153
## 3 0 0 0 0 0 0 0 127 251 251 253
## 4 0 0 55 148 210 253 253 113 87 148 55
## 5 0 40 152 244 252 253 224 211 252 232 40
## 6 0 0 144 253 252 71 0 0 0 0 0
## X56 X39 X0.175 X0.176 X0.177 X0.178 X0.179 X0.180 X0.181 X0.182 X0.183
## 1 122 0 0 0 0 0 0 0 0 0 0
## 2 210 40 0 0 0 0 0 0 0 0 0
## 3 62 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0
## X0.184 X0.185 X0.186 X18.3 X219 X253.14 X253.15 X253.16 X253.17 X253.18
## 1 0 0 0 0 0 0 0 163 252 252
## 2 220 163 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 87 232 252
## 5 0 0 0 0 0 15 152 239 252 252
## 6 0 0 0 0 0 0 0 16 191 253
## X198 X182 X247.1 X241 X0.187 X0.188 X0.189 X0.190 X0.191 X0.192 X0.193
## 1 252 253 252 252 96 189 253 167 0 0 0
## 2 0 0 0 0 0 27 254 162 0 0 0
## 3 0 0 68 236 251 211 31 8 0 0 0
## 4 253 189 210 252 252 253 168 0 0 0 0
## 5 252 216 31 37 252 252 60 0 0 0 0
## 6 252 71 0 0 0 0 0 0 0 0 0
## X0.194 X0.195 X0.196 X0.197 X0.198 X0.199 X0.200 X0.201 X0.202 X0.203
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 222 163 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.204 X80 X156 X107 X253.19 X253.20 X205 X11 X0.205 X43 X154.1 X0.206
## 1 0 0 0 51 238 253 253 190 114 253 228 47
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 60 228 251 251
## 4 0 0 4 57 242 252 190 65 5 12 182 252
## 5 0 0 96 252 252 252 252 217 29 0 37 252
## 6 0 0 0 0 26 221 253 252 124 31 0 0
## X0.207 X0.208 X0.209 X0.210 X0.211 X0.212 X0.213 X0.214 X0.215 X0.216
## 1 79 255 168 0 0 0 0 0 0 0
## 2 183 254 125 0 0 0 0 0 0 0
## 3 94 0 0 0 0 0 0 0 0 0
## 4 253 116 0 0 0 0 0 0 0 0
## 5 252 60 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.217 X0.218 X0.219 X0.220 X0.221 X0.222 X0.223 X0.224 X14 X1 X154.2
## 1 0 0 0 0 0 0 0 0 48 238 252
## 2 0 0 46 245 163 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 96 252 252
## 5 0 0 0 0 0 0 0 0 181 252 252
## 6 0 0 0 0 0 0 0 0 0 0 0
## X253.21 X90 X0.225 X0.226 X0.227 X0.228 X0.229 X0.230 X0.231 X0.232
## 1 252 179 12 75 121 21 0 0 253 243
## 2 0 0 0 0 0 0 0 198 254 56
## 3 0 0 0 155 253 253 189 0 0 0
## 4 183 14 0 0 92 252 252 225 21 0
## 5 220 167 30 0 0 77 252 252 60 0
## 6 125 253 252 252 108 0 0 0 0 0
## X0.233 X0.234 X0.235 X0.236 X0.237 X0.238 X0.239 X0.240 X0.241 X0.242
## 1 50 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 120
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.243 X0.244 X0.245 X0.246 X0.247 X0.248 X0.249 X139 X253.22 X190 X2
## 1 0 0 0 0 38 165 253 233 208 84 0
## 2 254 163 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 20
## 4 0 0 0 0 132 253 252 146 14 0 0
## 5 0 0 0 0 0 26 128 58 22 0 0
## 6 0 0 0 0 0 0 0 0 0 253 252
## X0.250 X0.251 X0.252 X0.253 X0.254 X0.255 X0.256 X0.257 X0.258 X0.259
## 1 0 0 0 0 0 253 252 165 0 0
## 2 0 0 0 23 231 254 29 0 0 0
## 3 253 251 235 66 0 0 0 0 0 0
## 4 0 215 252 252 79 0 0 0 0 0
## 5 0 0 100 252 252 60 0 0 0 0
## 6 252 108 0 0 0 0 0 0 0 0
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## 1 0 0 0 0 0 0 0 0 0 0
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## 1 7 178 252 240 71 19 28 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 32 205 253 251
## 4 126 253 247 176 9 0 0 8 78 245
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 255 253 253 108
## X0.276 X0.277 X0.278 X0.279 X0.280 X0.281 X0.282 X0.283 X0.284 X0.285
## 1 0 0 0 253 252 195 0 0 0 0
## 2 0 163 254 216 16 0 0 0 0 0
## 3 126 0 0 0 0 0 0 0 0 0
## 4 253 129 0 0 0 0 0 0 0 0
## 5 157 252 252 60 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
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## 1 0 0 0 0 0 0 0 0 57 252
## 2 0 0 0 0 159 254 67 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
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## 1 252 63 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 14 86 178 248 254
## 3 0 0 0 0 104 251 253 184 15 0 0
## 4 176 0 0 0 36 201 252 252 169 11 0
## 5 0 0 0 0 110 121 122 121 202 252 194
## 6 0 0 0 0 253 252 252 108 0 0 0
## X0.301 X0.302 X0.303 X0.304 X0.305 X0.306 X0.307 X0.308 X0.309 X0.310
## 1 253 252 195 0 0 0 0 0 0 0
## 2 91 0 0 0 0 0 0 0 0 0
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## 1 0 0 0 0 0 198 253 190 0 0
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## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 22 252 252 30 22 119
## 5 0 0 0 0 0 0 0 0 10 53
## 6 0 0 0 0 0 0 0 0 0 0
## X0.321 X81 X240 X253.24 X253.25 X119 X25 X0.322 X0.323 X0.324 X0.325
## 1 0 0 0 0 0 0 0 0 255 253 196
## 2 144 150 241 243 234 179 241 252 40 0 0
## 3 80 240 251 193 23 0 0 0 0 0 0
## 4 197 241 253 252 251 77 0 0 0 0 0
## 5 179 253 253 255 253 253 228 35 0 0 0
## 6 0 253 252 252 108 0 0 0 0 0 0
## X0.326 X0.327 X0.328 X0.329 X0.330 X0.331 X0.332 X0.333 X0.334 X0.335
## 1 0 0 0 0 0 0 0 0 0 0
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## 3 0 0 0 0 0 0 0 0 0 0
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## 6 0 0 0 0 0 0 0 0 0 0
## X0.336 X0.337 X0.338 X0.339 X0.340 X0.341 X0.342 X0.343 X0.344 X45 X186
## 1 0 76 246 252 112 0 0 0 0 0 0
## 2 237 207 207 207 253 254 250 240 198 143 91
## 3 0 0 0 0 0 0 32 253 253 253 159
## 4 0 16 231 252 253 252 252 252 226 227 252
## 5 0 0 0 5 54 227 252 243 228 170 242
## 6 0 0 0 0 0 0 0 0 253 252 252
## X253.26 X253.27 X150 X27 X0.345 X0.346 X0.347 X0.348 X0.349 X0.350
## 1 0 0 0 0 253 252 148 0 0 0
## 2 28 5 233 250 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
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## 5 252 252 231 117 6 0 0 0 0 0
## 6 108 0 0 0 0 0 0 0 0 0
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## 1 0 0 0 0 0 0 0 0 85 252
## 2 0 0 0 0 0 0 119 177 177 177
## 3 0 0 0 0 0 0 0 0 0 0
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## 5 0 0 0 0 0 0 0 0 0 6
## 6 0 0 0 0 0 0 0 0 0 0
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## 1 230 25 0 0 0 0 0 0 0 0 7
## 2 177 177 98 56 0 0 0 0 0 102 254
## 3 0 0 0 151 251 251 251 39 0 0 0
## 4 235 253 217 138 42 24 192 252 143 0 0
## 5 78 252 252 125 59 0 18 208 252 252 252
## 6 0 0 0 0 0 255 253 253 170 0 0
## X187 X0.368 X0.369 X0.370 X0.371 X0.372 X0.373 X0.374 X0.375 X0.376
## 1 135 253 186 12 0 0 0 0 0 0
## 2 220 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
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## 6 0 0 0 0 0 0 0 0 0 0
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## 1 0 0 0 0 0 85 252 223 0 0
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## 4 0 0 0 0 0 0 0 0 0 0
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## 6 0 0 0 0 0 0 0 0 0 0
## X0.387 X0.388 X0.389 X0.390 X0.391 X0.392 X249 X253.29 X249.1 X64.1
## 1 0 0 0 0 0 0 7 131 252 225
## 2 0 0 0 0 0 0 169 254 137 0
## 3 221 251 251 172 0 0 0 0 0 0
## 4 0 0 62 255 253 109 0 0 0 0
## 5 16 0 21 203 253 247 129 173 252 252
## 6 0 0 253 252 252 252 42 0 0 0
## X0.393 X0.394 X0.395 X0.396 X0.397 X0.398 X0.399 X0.400 X0.401 X0.402
## 1 71 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
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## 6 0 0 0 0 0 0 0 0 0 0
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## 1 0 0 0 85 252 145 0 0 0 0
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## 6 0 0 0 0 0 0 0 0 0 0
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## 3 196 12 0 0 0 0 0 0 0 0 0
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## 6 149 252 252 252 144 0 0 0 0 0 0
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## 1 0 0 0 0 0 0 0 0 0 0
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## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 253 251 251 89 0 0
## 4 0 0 0 0 0 0 0 0 253 252
## 5 252 242 88 18 73 170 244 252 126 29
## 6 0 0 0 0 0 0 0 109 252 252
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## 1 238 253 162 0 0 0 0 0 0 0
## 2 0 169 254 57 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 21 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 89 180 180 37 0
## 6 252 144 0 0 0 0 0 0 0 0
## X0.439 X0.440 X0.441 X0.442 X0.443 X0.444 X0.445 X0.446 X0.447 X0.448
## 1 0 0 0 0 0 0 0 0 85 252
## 2 0 0 0 0 0 0 0 0 0 0
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## 5 0 0 0 0 0 0 0 231 252 245
## 6 0 0 0 0 0 0 0 0 0 0
## X0.449 X0.450 X24 X114 X221 X253.35 X253.36 X253.37 X253.38 X201 X78
## 1 249 146 48 29 85 178 225 253 223 167 56
## 2 0 0 0 0 0 0 0 0 0 169 255
## 3 0 159 255 253 253 31 0 0 0 0 0
## 4 0 0 0 0 0 71 253 252 21 0 0
## 5 205 216 252 252 252 124 3 0 0 0 0
## 6 0 0 0 0 0 0 218 253 253 255 35
## X0.451 X0.452 X0.453 X0.454 X0.455 X0.456 X0.457 X0.458 X0.459 X0.460
## 1 0 0 0 0 0 0 0 0 0 0
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## X0.461 X0.462 X0.463 X0.464 X0.465 X0.466 X0.467 X23 X66 X213 X253.39
## 1 0 0 0 0 0 85 252 252 252 229 215
## 2 0 0 0 0 0 0 0 0 0 0 0
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## 4 0 0 0 0 0 0 0 0 0 0 0
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## 6 0 0 0 0 0 0 0 0 0 0 0
## X253.40 X253.41 X253.42 X198.1 X81.1 X2.2 X0.468 X0.469 X0.470 X0.471
## 1 252 252 252 196 130 0 0 0 0 0
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## 5 36 4 0 0 0 0 0 0 0 0
## 6 0 0 175 252 252 253 35 0 0 0
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## 1 0 0 0 0 0 0 0 0 0 0
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## 1 0 0 28 199 252 252 253 252 252 233
## 2 0 0 0 0 0 0 0 0 0 0
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## 4 0 0 0 0 0 0 0 0 0 45
## 5 0 13 93 143 121 23 6 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
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## 3 0 0 0 0 0 0 0 0 0 0 0
## 4 255 253 21 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0
## 6 73 252 252 253 35 0 0 0 0 0 0
## X0.493 X0.494 X0.495 X0.496 X0.497 X0.498 X0.499 X55 X172.1 X226 X253.47
## 1 0 0 0 0 0 0 0 0 0 0 25
## 2 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0
## X253.48 X253.49 X253.50 X244 X133 X11.2 X0.500 X0.501 X0.502 X0.503
## 1 128 252 253 252 141 37 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 169
## 3 64 251 253 220 0 0 0 0 0 0
## 4 0 0 0 0 0 0 218 252 56 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 31 211 252 253
## X0.504 X0.505 X0.506 X0.507 X0.508 X0.509 X0.510 X0.511 X0.512 X0.513
## 1 0 0 0 0 0 0 0 0 0 0
## 2 255 153 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 35 0 0 0 0 0 0 0 0 0
## X0.514 X0.515 X0.516 X0.517 X136.1 X253.51 X253.52 X253.53 X212 X135
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 24 193
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X132 X16.1 X0.518 X0.519 X0.520 X0.521 X0.522 X0.523 X0.524 X0.525
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 96 254 153
## 3 253 220 0 0 0 0 0 0 0 0
## 4 0 0 0 0 96 252 189 42 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.526 X0.527 X0.528 X0.529 X0.530 X0.531 X0.532 X0.533 X0.534 X0.535
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.536 X0.537 X0.538 X0.539 X0.540 X0.541 X0.542 X0.543 X0.544 X0.545
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.546 X0.547 X0.548 X0.549 X0.550 X0.551 X0.552 X0.553 X0.554 X0.555
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 14 184 252 170 11 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.556 X0.557 X0.558 X0.559 X0.560 X0.561 X0.562 X0.563 X0.564 X0.565
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.566 X0.567 X0.568 X0.569 X0.570 X0.571 X0.572 X0.573 X0.574 X0.575
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.576 X0.577 X0.578 X0.579 X0.580 X0.581 X0.582 X0.583 X0.584 X0.585
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 14 147 252 42 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.586 X0.587 X0.588 X0.589 X0.590 X0.591 X0.592 X0.593 X0.594 X0.595
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.596 X0.597 X0.598 X0.599 X0.600 X0.601 X0.602 X0.603 X0.604 X0.605
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.606 X0.607 X0.608 X0.609 X0.610 X0.611 X0.612 X0.613 X0.614 X0.615
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.616 X0.617
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0dim(DATASET.train) ## gives dimentions## [1] 59999 785nrow(DATASET.train) ## gives no of row are there## [1] 59999ncol(DATASET.train) ##gives no of col. are there## [1] 785colnames(DATASET.train) ## what are the col. names## [1] "X5" "X0" "X0.1" "X0.2" "X0.3" "X0.4" "X0.5"
## [8] "X0.6" "X0.7" "X0.8" "X0.9" "X0.10" "X0.11" "X0.12"
## [15] "X0.13" "X0.14" "X0.15" "X0.16" "X0.17" "X0.18" "X0.19"
## [22] "X0.20" "X0.21" "X0.22" "X0.23" "X0.24" "X0.25" "X0.26"
## [29] "X0.27" "X0.28" "X0.29" "X0.30" "X0.31" "X0.32" "X0.33"
## [36] "X0.34" "X0.35" "X0.36" "X0.37" "X0.38" "X0.39" "X0.40"
## [43] "X0.41" "X0.42" "X0.43" "X0.44" "X0.45" "X0.46" "X0.47"
## [50] "X0.48" "X0.49" "X0.50" "X0.51" "X0.52" "X0.53" "X0.54"
## [57] "X0.55" "X0.56" "X0.57" "X0.58" "X0.59" "X0.60" "X0.61"
## [64] "X0.62" "X0.63" "X0.64" "X0.65" "X0.66" "X0.67" "X0.68"
## [71] "X0.69" "X0.70" "X0.71" "X0.72" "X0.73" "X0.74" "X0.75"
## [78] "X0.76" "X0.77" "X0.78" "X0.79" "X0.80" "X0.81" "X0.82"
## [85] "X0.83" "X0.84" "X0.85" "X0.86" "X0.87" "X0.88" "X0.89"
## [92] "X0.90" "X0.91" "X0.92" "X0.93" "X0.94" "X0.95" "X0.96"
## [99] "X0.97" "X0.98" "X0.99" "X0.100" "X0.101" "X0.102" "X0.103"
## [106] "X0.104" "X0.105" "X0.106" "X0.107" "X0.108" "X0.109" "X0.110"
## [113] "X0.111" "X0.112" "X0.113" "X0.114" "X0.115" "X0.116" "X0.117"
## [120] "X0.118" "X0.119" "X0.120" "X0.121" "X0.122" "X0.123" "X0.124"
## [127] "X0.125" "X0.126" "X0.127" "X0.128" "X0.129" "X0.130" "X0.131"
## [134] "X0.132" "X0.133" "X0.134" "X0.135" "X0.136" "X0.137" "X0.138"
## [141] "X0.139" "X0.140" "X0.141" "X0.142" "X0.143" "X0.144" "X0.145"
## [148] "X0.146" "X0.147" "X0.148" "X0.149" "X0.150" "X0.151" "X3"
## [155] "X18" "X18.1" "X18.2" "X126" "X136" "X175" "X26"
## [162] "X166" "X255" "X247" "X127" "X0.152" "X0.153" "X0.154"
## [169] "X0.155" "X0.156" "X0.157" "X0.158" "X0.159" "X0.160" "X0.161"
## [176] "X0.162" "X0.163" "X30" "X36" "X94" "X154" "X170"
## [183] "X253" "X253.1" "X253.2" "X253.3" "X253.4" "X225" "X172"
## [190] "X253.5" "X242" "X195" "X64" "X0.164" "X0.165" "X0.166"
## [197] "X0.167" "X0.168" "X0.169" "X0.170" "X0.171" "X0.172" "X0.173"
## [204] "X0.174" "X49" "X238" "X253.6" "X253.7" "X253.8" "X253.9"
## [211] "X253.10" "X253.11" "X253.12" "X253.13" "X251" "X93" "X82"
## [218] "X82.1" "X56" "X39" "X0.175" "X0.176" "X0.177" "X0.178"
## [225] "X0.179" "X0.180" "X0.181" "X0.182" "X0.183" "X0.184" "X0.185"
## [232] "X0.186" "X18.3" "X219" "X253.14" "X253.15" "X253.16" "X253.17"
## [239] "X253.18" "X198" "X182" "X247.1" "X241" "X0.187" "X0.188"
## [246] "X0.189" "X0.190" "X0.191" "X0.192" "X0.193" "X0.194" "X0.195"
## [253] "X0.196" "X0.197" "X0.198" "X0.199" "X0.200" "X0.201" "X0.202"
## [260] "X0.203" "X0.204" "X80" "X156" "X107" "X253.19" "X253.20"
## [267] "X205" "X11" "X0.205" "X43" "X154.1" "X0.206" "X0.207"
## [274] "X0.208" "X0.209" "X0.210" "X0.211" "X0.212" "X0.213" "X0.214"
## [281] "X0.215" "X0.216" "X0.217" "X0.218" "X0.219" "X0.220" "X0.221"
## [288] "X0.222" "X0.223" "X0.224" "X14" "X1" "X154.2" "X253.21"
## [295] "X90" "X0.225" "X0.226" "X0.227" "X0.228" "X0.229" "X0.230"
## [302] "X0.231" "X0.232" "X0.233" "X0.234" "X0.235" "X0.236" "X0.237"
## [309] "X0.238" "X0.239" "X0.240" "X0.241" "X0.242" "X0.243" "X0.244"
## [316] "X0.245" "X0.246" "X0.247" "X0.248" "X0.249" "X139" "X253.22"
## [323] "X190" "X2" "X0.250" "X0.251" "X0.252" "X0.253" "X0.254"
## [330] "X0.255" "X0.256" "X0.257" "X0.258" "X0.259" "X0.260" "X0.261"
## [337] "X0.262" "X0.263" "X0.264" "X0.265" "X0.266" "X0.267" "X0.268"
## [344] "X0.269" "X0.270" "X0.271" "X0.272" "X0.273" "X11.1" "X190.1"
## [351] "X253.23" "X70" "X0.274" "X0.275" "X0.276" "X0.277" "X0.278"
## [358] "X0.279" "X0.280" "X0.281" "X0.282" "X0.283" "X0.284" "X0.285"
## [365] "X0.286" "X0.287" "X0.288" "X0.289" "X0.290" "X0.291" "X0.292"
## [372] "X0.293" "X0.294" "X0.295" "X0.296" "X0.297" "X0.298" "X35"
## [379] "X241.1" "X225.1" "X160" "X108" "X1.1" "X0.299" "X0.300"
## [386] "X0.301" "X0.302" "X0.303" "X0.304" "X0.305" "X0.306" "X0.307"
## [393] "X0.308" "X0.309" "X0.310" "X0.311" "X0.312" "X0.313" "X0.314"
## [400] "X0.315" "X0.316" "X0.317" "X0.318" "X0.319" "X0.320" "X0.321"
## [407] "X81" "X240" "X253.24" "X253.25" "X119" "X25" "X0.322"
## [414] "X0.323" "X0.324" "X0.325" "X0.326" "X0.327" "X0.328" "X0.329"
## [421] "X0.330" "X0.331" "X0.332" "X0.333" "X0.334" "X0.335" "X0.336"
## [428] "X0.337" "X0.338" "X0.339" "X0.340" "X0.341" "X0.342" "X0.343"
## [435] "X0.344" "X45" "X186" "X253.26" "X253.27" "X150" "X27"
## [442] "X0.345" "X0.346" "X0.347" "X0.348" "X0.349" "X0.350" "X0.351"
## [449] "X0.352" "X0.353" "X0.354" "X0.355" "X0.356" "X0.357" "X0.358"
## [456] "X0.359" "X0.360" "X0.361" "X0.362" "X0.363" "X0.364" "X0.365"
## [463] "X0.366" "X0.367" "X16" "X93.1" "X252" "X253.28" "X187"
## [470] "X0.368" "X0.369" "X0.370" "X0.371" "X0.372" "X0.373" "X0.374"
## [477] "X0.375" "X0.376" "X0.377" "X0.378" "X0.379" "X0.380" "X0.381"
## [484] "X0.382" "X0.383" "X0.384" "X0.385" "X0.386" "X0.387" "X0.388"
## [491] "X0.389" "X0.390" "X0.391" "X0.392" "X249" "X253.29" "X249.1"
## [498] "X64.1" "X0.393" "X0.394" "X0.395" "X0.396" "X0.397" "X0.398"
## [505] "X0.399" "X0.400" "X0.401" "X0.402" "X0.403" "X0.404" "X0.405"
## [512] "X0.406" "X0.407" "X0.408" "X0.409" "X0.410" "X0.411" "X0.412"
## [519] "X0.413" "X46" "X130" "X183" "X253.30" "X253.31" "X207"
## [526] "X2.1" "X0.414" "X0.415" "X0.416" "X0.417" "X0.418" "X0.419"
## [533] "X0.420" "X0.421" "X0.422" "X0.423" "X0.424" "X0.425" "X0.426"
## [540] "X0.427" "X0.428" "X0.429" "X0.430" "X0.431" "X0.432" "X39.1"
## [547] "X148" "X229" "X253.32" "X253.33" "X253.34" "X250" "X182.1"
## [554] "X0.433" "X0.434" "X0.435" "X0.436" "X0.437" "X0.438" "X0.439"
## [561] "X0.440" "X0.441" "X0.442" "X0.443" "X0.444" "X0.445" "X0.446"
## [568] "X0.447" "X0.448" "X0.449" "X0.450" "X24" "X114" "X221"
## [575] "X253.35" "X253.36" "X253.37" "X253.38" "X201" "X78" "X0.451"
## [582] "X0.452" "X0.453" "X0.454" "X0.455" "X0.456" "X0.457" "X0.458"
## [589] "X0.459" "X0.460" "X0.461" "X0.462" "X0.463" "X0.464" "X0.465"
## [596] "X0.466" "X0.467" "X23" "X66" "X213" "X253.39" "X253.40"
## [603] "X253.41" "X253.42" "X198.1" "X81.1" "X2.2" "X0.468" "X0.469"
## [610] "X0.470" "X0.471" "X0.472" "X0.473" "X0.474" "X0.475" "X0.476"
## [617] "X0.477" "X0.478" "X0.479" "X0.480" "X0.481" "X0.482" "X0.483"
## [624] "X18.4" "X171" "X219.1" "X253.43" "X253.44" "X253.45" "X253.46"
## [631] "X195.1" "X80.1" "X9" "X0.484" "X0.485" "X0.486" "X0.487"
## [638] "X0.488" "X0.489" "X0.490" "X0.491" "X0.492" "X0.493" "X0.494"
## [645] "X0.495" "X0.496" "X0.497" "X0.498" "X0.499" "X55" "X172.1"
## [652] "X226" "X253.47" "X253.48" "X253.49" "X253.50" "X244" "X133"
## [659] "X11.2" "X0.500" "X0.501" "X0.502" "X0.503" "X0.504" "X0.505"
## [666] "X0.506" "X0.507" "X0.508" "X0.509" "X0.510" "X0.511" "X0.512"
## [673] "X0.513" "X0.514" "X0.515" "X0.516" "X0.517" "X136.1" "X253.51"
## [680] "X253.52" "X253.53" "X212" "X135" "X132" "X16.1" "X0.518"
## [687] "X0.519" "X0.520" "X0.521" "X0.522" "X0.523" "X0.524" "X0.525"
## [694] "X0.526" "X0.527" "X0.528" "X0.529" "X0.530" "X0.531" "X0.532"
## [701] "X0.533" "X0.534" "X0.535" "X0.536" "X0.537" "X0.538" "X0.539"
## [708] "X0.540" "X0.541" "X0.542" "X0.543" "X0.544" "X0.545" "X0.546"
## [715] "X0.547" "X0.548" "X0.549" "X0.550" "X0.551" "X0.552" "X0.553"
## [722] "X0.554" "X0.555" "X0.556" "X0.557" "X0.558" "X0.559" "X0.560"
## [729] "X0.561" "X0.562" "X0.563" "X0.564" "X0.565" "X0.566" "X0.567"
## [736] "X0.568" "X0.569" "X0.570" "X0.571" "X0.572" "X0.573" "X0.574"
## [743] "X0.575" "X0.576" "X0.577" "X0.578" "X0.579" "X0.580" "X0.581"
## [750] "X0.582" "X0.583" "X0.584" "X0.585" "X0.586" "X0.587" "X0.588"
## [757] "X0.589" "X0.590" "X0.591" "X0.592" "X0.593" "X0.594" "X0.595"
## [764] "X0.596" "X0.597" "X0.598" "X0.599" "X0.600" "X0.601" "X0.602"
## [771] "X0.603" "X0.604" "X0.605" "X0.606" "X0.607" "X0.608" "X0.609"
## [778] "X0.610" "X0.611" "X0.612" "X0.613" "X0.614" "X0.615" "X0.616"
## [785] "X0.617"summary(DATASET.train)## X5 X0 X0.1 X0.2 X0.3
## Min. :0.000 Min. :0 Min. :0 Min. :0 Min. :0
## 1st Qu.:2.000 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0
## Median :4.000 Median :0 Median :0 Median :0 Median :0
## Mean :4.454 Mean :0 Mean :0 Mean :0 Mean :0
## 3rd Qu.:7.000 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
## Max. :9.000 Max. :0 Max. :0 Max. :0 Max. :0
## X0.4 X0.5 X0.6 X0.7 X0.8 X0.9
## Min. :0 Min. :0 Min. :0 Min. :0 Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0
## Median :0 Median :0 Median :0 Median :0 Median :0 Median :0
## Mean :0 Mean :0 Mean :0 Mean :0 Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0 Max. :0 Max. :0 Max. :0 Max. :0
## X0.10 X0.11 X0.12 X0.13
## Min. :0 Min. :0 Min. : 0.0000 Min. :0.00e+00
## 1st Qu.:0 1st Qu.:0 1st Qu.: 0.0000 1st Qu.:0.00e+00
## Median :0 Median :0 Median : 0.0000 Median :0.00e+00
## Mean :0 Mean :0 Mean : 0.0021 Mean :7.83e-03
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.: 0.0000 3rd Qu.:0.00e+00
## Max. :0 Max. :0 Max. :116.0000 Max. :2.54e+02
## X0.14 X0.15 X0.16 X0.17 X0.18
## Min. : 0.0000 Min. :0.00000 Min. :0 Min. :0 Min. :0
## 1st Qu.: 0.0000 1st Qu.:0.00000 1st Qu.:0 1st Qu.:0 1st Qu.:0
## Median : 0.0000 Median :0.00000 Median :0 Median :0 Median :0
## Mean : 0.0036 Mean :0.00015 Mean :0 Mean :0 Mean :0
## 3rd Qu.: 0.0000 3rd Qu.:0.00000 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
## Max. :216.0000 Max. :9.00000 Max. :0 Max. :0 Max. :0
## X0.19 X0.20 X0.21 X0.22 X0.23 X0.24
## Min. :0 Min. :0 Min. :0 Min. :0 Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0
## Median :0 Median :0 Median :0 Median :0 Median :0 Median :0
## Mean :0 Mean :0 Mean :0 Mean :0 Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0 Max. :0 Max. :0 Max. :0 Max. :0
## X0.25 X0.26 X0.27 X0.28 X0.29 X0.30
## Min. :0 Min. :0 Min. :0 Min. :0 Min. :0 Min. :0
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## Mean :0 Mean :0 Mean :0 Mean :0 Mean :0 Mean :0
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## Median :0 Median :0.00e+00 Median :0.0e+00 Median :0.00e+00
## Mean :0 Mean :2.67e-04 Mean :9.2e-04 Mean :9.28e-03
## 3rd Qu.:0 3rd Qu.:0.00e+00 3rd Qu.:0.0e+00 3rd Qu.:0.00e+00
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## Median : 0.0000 Median : 0.0000 Median : 0.0000
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## Max. :255.0000 Max. :255.0000 Max. :255.0000
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## Mean : 0.0007 Mean :6.95e-03 Mean : 0.00548
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## Median : 0.0000 Median : 0.0000 Median : 0.0000
## Mean : 0.0471 Mean : 0.1384 Mean : 0.2642
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## Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000
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## Median : 0.0000 Median : 0.0000 Median : 0.00000
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## Median : 0.00000 Median : 0.00000 Median : 0.0000
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## Median : 0.0000 Median : 0.000 Median : 0.000 Median : 0.00
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## Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.00
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## Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000
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## Median : 0.0000 Median : 0.0000 Median : 0.00000
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## Median : 0.0000 Median :0 Median :0 Median :0.0e+00
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## Median :0.00e+00 Median : 0.00000 Median : 0.00000
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## Median : 0.0000 Median : 0.000 Median : 0.000 Median : 0.000
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## Median : 0.000 Median : 0.00 Median : 0.00 Median : 0.00
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## Median : 0.00 Median : 0.00 Median : 0.00 Median : 0.00
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## Median : 0.00 Median : 0.00 Median : 0.00 Median : 0.00
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## Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.0000
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## Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.00
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## Max. :255.000 Max. :255.000 Max. :255.000 Max. :255.00
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## Median : 0.00 Median : 0.0 Median : 0.00 Median : 0.0
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## Max. :255.00 Max. :255.0 Max. :255.00 Max. :255.0
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## Max. :255.00 Max. :255.0 Max. :255.0 Max. :255.00
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## Max. :255.00 Max. :255.00 Max. :255.00 Max. :255.00
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## Mean :106.2 Mean :118.5 Mean :124.1 Mean :121.7
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## Max. :255.0 Max. :255.0 Max. :255.0 Max. :255.0
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## Median : 0.00000 Median : 0.0000 Median : 0.0000
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## Median : 0.000 Median : 0.000 Median : 0.00 Median : 0.00
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## Max. :255.00 Max. :255.0 Max. :255.00 Max. :255.0
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## Max. :255.0 Max. :255 Max. :255.0 Max. :255.0
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## Max. :255.0 Max. :255.0 Max. :255.0 Max. :255.00
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## Max. :255.00 Max. :255.00 Max. :255.00 Max. :255.0
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## Max. :255.000 Max. :255.00 Max. :255.00 Max. :255.0
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## Max. :255.00 Max. :255.00 Max. :255.00 Max. :255.0
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## Median : 64.0 Median : 0.00 Median : 0.00 Median : 0.00
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## 3rd Qu.:0.00e+00 3rd Qu.: 0.00000 3rd Qu.: 0.0000
## Max. :1.88e+02 Max. :254.00000 Max. :255.0000
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## Max. :255.00 Max. :255.0 Max. :255.0 Max. :255.0
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## Max. :255.0 Max. :255.0 Max. :255 Max. :255.00
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## Mean : 91.38 Mean : 94.24 Mean :105 Mean :123.2
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## Max. :255.0 Max. :255.0 Max. :255.0 Max. :255.0
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## Median : 41.0 Median : 0.00 Median : 0.00 Median : 0.00
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## Max. :255.0 Max. :255.00 Max. :255.00 Max. :255.00
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## Median : 0.00 Median : 0.00 Median : 0.000 Median : 0.00
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## Median :0.00e+00 Median : 0.00000 Median : 0.000 Median : 0.00
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## Max. :1.07e+02 Max. :254.00000 Max. :255.000 Max. :255.00
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## Max. :255.00 Max. :255.00 Max. :255.00 Max. :255.0
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## Mean : 89.59 Mean :102 Mean :112.6 Mean :115.4
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## Max. :255.00 Max. :255 Max. :255.0 Max. :255.0
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## Max. :255.0 Max. :255.0 Max. :255.00 Max. :255.00
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## Median : 0.00 Median : 0.00 Median : 0.00 Median : 0.00
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## Max. :255.000 Max. :255.00 Max. :255.00 Max. :255.00
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## Mean : 58.28 Mean : 66 Mean : 71.75 Mean : 76.58
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## Mean : 83.12 Mean : 95.13 Mean :106.6 Mean :112.2
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## Max. :255.00 Max. :255.00 Max. :255.0 Max. :255.0
## X183 X253.30 X253.31 X207
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## Max. :255.0 Max. :255 Max. :255.00 Max. :255.00
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## Mean : 77.88 Mean : 82.64 Mean : 89.97 Mean :101.4
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## Mean :112.4 Mean :117.8 Mean :115.9 Mean :107.4
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## Max. :255.0 Max. :255.0 Max. :255.0 Max. :255.0
## X250 X182.1 X0.433 X0.434
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## Median : 0.00 Median : 0.000 Median : 0.00 Median : 0.000
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## Max. :255 Max. :255.0 Max. :255.0 Max. :255.0
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## Max. :255.00 Max. :255.00 Max. :255.00 Max. :255.00
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## Max. :255.00 Max. :255 Max. :255.0 Max. :255.0
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## Max. :255.0 Max. :255.0 Max. :255.0 Max. :255
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## Max. :255.00 Max. :255 Max. :255.00 Max. :255.0
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## Max. :255.0 Max. :255.0 Max. :255.0 Max. :255.0
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## Min. : 0.00 Min. : 0.00 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 0.00 Median : 0.00 Median : 0.000 Median : 0.000
## Mean : 4.85 Mean : 5.98 Mean : 6.445 Mean : 6.259
## 3rd Qu.: 0.00 3rd Qu.: 0.00 3rd Qu.: 0.000 3rd Qu.: 0.000
## Max. :255.00 Max. :255.00 Max. :255.000 Max. :255.000
## X0.576 X0.577 X0.578 X0.579
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000
## Mean : 5.571 Mean : 4.451 Mean : 3.535 Mean : 2.592
## 3rd Qu.: 0.000 3rd Qu.: 0.000 3rd Qu.: 0.000 3rd Qu.: 0.000
## Max. :255.000 Max. :255.000 Max. :255.000 Max. :255.000
## X0.580 X0.581 X0.582 X0.583
## Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.0000
## 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.0000
## Median : 0.000 Median : 0.000 Median : 0.0000 Median : 0.0000
## Mean : 1.701 Mean : 1.009 Mean : 0.5403 Mean : 0.2384
## 3rd Qu.: 0.000 3rd Qu.: 0.000 3rd Qu.: 0.0000 3rd Qu.: 0.0000
## Max. :255.000 Max. :255.000 Max. :255.0000 Max. :255.0000
## X0.584 X0.585 X0.586
## Min. : 0.00000 Min. : 0.00000 Min. :0.00e+00
## 1st Qu.: 0.00000 1st Qu.: 0.00000 1st Qu.:0.00e+00
## Median : 0.00000 Median : 0.00000 Median :0.00e+00
## Mean : 0.07527 Mean : 0.01617 Mean :5.17e-04
## 3rd Qu.: 0.00000 3rd Qu.: 0.00000 3rd Qu.:0.00e+00
## Max. :255.00000 Max. :137.00000 Max. :2.80e+01
## X0.587 X0.588 X0.589 X0.590 X0.591
## Min. :0.0e+00 Min. :0 Min. :0 Min. :0 Min. :0
## 1st Qu.:0.0e+00 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0
## Median :0.0e+00 Median :0 Median :0 Median :0 Median :0
## Mean :9.8e-04 Mean :0 Mean :0 Mean :0 Mean :0
## 3rd Qu.:0.0e+00 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
## Max. :5.9e+01 Max. :0 Max. :0 Max. :0 Max. :0
## X0.592 X0.593 X0.594 X0.595
## Min. :0 Min. :0 Min. : 0.00000 Min. : 0.00000
## 1st Qu.:0 1st Qu.:0 1st Qu.: 0.00000 1st Qu.: 0.00000
## Median :0 Median :0 Median : 0.00000 Median : 0.00000
## Mean :0 Mean :0 Mean : 0.00253 Mean : 0.01558
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.: 0.00000 3rd Qu.: 0.00000
## Max. :0 Max. :0 Max. :94.00000 Max. :252.00000
## X0.596 X0.597 X0.598
## Min. : 0.00000 Min. : 0.0000 Min. : 0.0000
## 1st Qu.: 0.00000 1st Qu.: 0.0000 1st Qu.: 0.0000
## Median : 0.00000 Median : 0.0000 Median : 0.0000
## Mean : 0.04163 Mean : 0.0892 Mean : 0.1282
## 3rd Qu.: 0.00000 3rd Qu.: 0.0000 3rd Qu.: 0.0000
## Max. :245.00000 Max. :254.0000 Max. :254.0000
## X0.599 X0.600 X0.601 X0.602
## Min. : 0.0000 Min. : 0.000 Min. : 0.00 Min. : 0.000
## 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.: 0.000
## Median : 0.0000 Median : 0.000 Median : 0.00 Median : 0.000
## Mean : 0.1967 Mean : 0.336 Mean : 0.43 Mean : 0.526
## 3rd Qu.: 0.0000 3rd Qu.: 0.000 3rd Qu.: 0.00 3rd Qu.: 0.000
## Max. :255.0000 Max. :255.000 Max. :255.00 Max. :255.000
## X0.603 X0.604 X0.605
## Min. : 0.0000 Min. : 0.000 Min. : 0.0000
## 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000
## Median : 0.0000 Median : 0.000 Median : 0.0000
## Mean : 0.5907 Mean : 0.688 Mean : 0.5921
## 3rd Qu.: 0.0000 3rd Qu.: 0.000 3rd Qu.: 0.0000
## Max. :255.0000 Max. :255.000 Max. :255.0000
## X0.606 X0.607 X0.608
## Min. : 0.0000 Min. : 0.0000 Min. : 0.0000
## 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.0000
## Median : 0.0000 Median : 0.0000 Median : 0.0000
## Mean : 0.4827 Mean : 0.3435 Mean : 0.2004
## 3rd Qu.: 0.0000 3rd Qu.: 0.0000 3rd Qu.: 0.0000
## Max. :255.0000 Max. :255.0000 Max. :254.0000
## X0.609 X0.610 X0.611
## Min. : 0.00000 Min. : 0.00000 Min. : 0.00000
## 1st Qu.: 0.00000 1st Qu.: 0.00000 1st Qu.: 0.00000
## Median : 0.00000 Median : 0.00000 Median : 0.00000
## Mean : 0.08887 Mean : 0.04563 Mean : 0.01928
## 3rd Qu.: 0.00000 3rd Qu.: 0.00000 3rd Qu.: 0.00000
## Max. :254.00000 Max. :253.00000 Max. :253.00000
## X0.612 X0.613 X0.614 X0.615 X0.616
## Min. : 0.00000 Min. : 0.000 Min. :0 Min. :0 Min. :0
## 1st Qu.: 0.00000 1st Qu.: 0.000 1st Qu.:0 1st Qu.:0 1st Qu.:0
## Median : 0.00000 Median : 0.000 Median :0 Median :0 Median :0
## Mean : 0.01512 Mean : 0.002 Mean :0 Mean :0 Mean :0
## 3rd Qu.: 0.00000 3rd Qu.: 0.000 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
## Max. :254.00000 Max. :62.000 Max. :0 Max. :0 Max. :0
## X0.617
## Min. :0
## 1st Qu.:0
## Median :0
## Mean :0
## 3rd Qu.:0
## Max. :0sapply(DATASET.train[1, ], class) ## we convert the dataframe into class format. ## X5 X0 X0.1 X0.2 X0.3 X0.4 X0.5
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.6 X0.7 X0.8 X0.9 X0.10 X0.11 X0.12
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.13 X0.14 X0.15 X0.16 X0.17 X0.18 X0.19
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.20 X0.21 X0.22 X0.23 X0.24 X0.25 X0.26
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.27 X0.28 X0.29 X0.30 X0.31 X0.32 X0.33
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.34 X0.35 X0.36 X0.37 X0.38 X0.39 X0.40
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.41 X0.42 X0.43 X0.44 X0.45 X0.46 X0.47
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.48 X0.49 X0.50 X0.51 X0.52 X0.53 X0.54
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.55 X0.56 X0.57 X0.58 X0.59 X0.60 X0.61
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.62 X0.63 X0.64 X0.65 X0.66 X0.67 X0.68
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.69 X0.70 X0.71 X0.72 X0.73 X0.74 X0.75
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.76 X0.77 X0.78 X0.79 X0.80 X0.81 X0.82
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.83 X0.84 X0.85 X0.86 X0.87 X0.88 X0.89
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.90 X0.91 X0.92 X0.93 X0.94 X0.95 X0.96
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.97 X0.98 X0.99 X0.100 X0.101 X0.102 X0.103
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.104 X0.105 X0.106 X0.107 X0.108 X0.109 X0.110
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.111 X0.112 X0.113 X0.114 X0.115 X0.116 X0.117
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.118 X0.119 X0.120 X0.121 X0.122 X0.123 X0.124
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.125 X0.126 X0.127 X0.128 X0.129 X0.130 X0.131
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.132 X0.133 X0.134 X0.135 X0.136 X0.137 X0.138
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.139 X0.140 X0.141 X0.142 X0.143 X0.144 X0.145
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.146 X0.147 X0.148 X0.149 X0.150 X0.151 X3
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X18 X18.1 X18.2 X126 X136 X175 X26
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X166 X255 X247 X127 X0.152 X0.153 X0.154
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.155 X0.156 X0.157 X0.158 X0.159 X0.160 X0.161
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.162 X0.163 X30 X36 X94 X154 X170
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X253 X253.1 X253.2 X253.3 X253.4 X225 X172
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X253.5 X242 X195 X64 X0.164 X0.165 X0.166
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.167 X0.168 X0.169 X0.170 X0.171 X0.172 X0.173
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.174 X49 X238 X253.6 X253.7 X253.8 X253.9
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X253.10 X253.11 X253.12 X253.13 X251 X93 X82
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X82.1 X56 X39 X0.175 X0.176 X0.177 X0.178
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.179 X0.180 X0.181 X0.182 X0.183 X0.184 X0.185
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.186 X18.3 X219 X253.14 X253.15 X253.16 X253.17
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X253.18 X198 X182 X247.1 X241 X0.187 X0.188
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.189 X0.190 X0.191 X0.192 X0.193 X0.194 X0.195
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.196 X0.197 X0.198 X0.199 X0.200 X0.201 X0.202
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.203 X0.204 X80 X156 X107 X253.19 X253.20
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X205 X11 X0.205 X43 X154.1 X0.206 X0.207
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.208 X0.209 X0.210 X0.211 X0.212 X0.213 X0.214
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.215 X0.216 X0.217 X0.218 X0.219 X0.220 X0.221
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.222 X0.223 X0.224 X14 X1 X154.2 X253.21
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X90 X0.225 X0.226 X0.227 X0.228 X0.229 X0.230
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.231 X0.232 X0.233 X0.234 X0.235 X0.236 X0.237
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.238 X0.239 X0.240 X0.241 X0.242 X0.243 X0.244
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.245 X0.246 X0.247 X0.248 X0.249 X139 X253.22
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X190 X2 X0.250 X0.251 X0.252 X0.253 X0.254
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.255 X0.256 X0.257 X0.258 X0.259 X0.260 X0.261
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.262 X0.263 X0.264 X0.265 X0.266 X0.267 X0.268
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.269 X0.270 X0.271 X0.272 X0.273 X11.1 X190.1
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X253.23 X70 X0.274 X0.275 X0.276 X0.277 X0.278
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.279 X0.280 X0.281 X0.282 X0.283 X0.284 X0.285
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.286 X0.287 X0.288 X0.289 X0.290 X0.291 X0.292
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.293 X0.294 X0.295 X0.296 X0.297 X0.298 X35
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X241.1 X225.1 X160 X108 X1.1 X0.299 X0.300
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.301 X0.302 X0.303 X0.304 X0.305 X0.306 X0.307
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.308 X0.309 X0.310 X0.311 X0.312 X0.313 X0.314
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.315 X0.316 X0.317 X0.318 X0.319 X0.320 X0.321
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X81 X240 X253.24 X253.25 X119 X25 X0.322
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.323 X0.324 X0.325 X0.326 X0.327 X0.328 X0.329
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.330 X0.331 X0.332 X0.333 X0.334 X0.335 X0.336
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.337 X0.338 X0.339 X0.340 X0.341 X0.342 X0.343
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.344 X45 X186 X253.26 X253.27 X150 X27
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.345 X0.346 X0.347 X0.348 X0.349 X0.350 X0.351
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.352 X0.353 X0.354 X0.355 X0.356 X0.357 X0.358
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.359 X0.360 X0.361 X0.362 X0.363 X0.364 X0.365
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.366 X0.367 X16 X93.1 X252 X253.28 X187
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.368 X0.369 X0.370 X0.371 X0.372 X0.373 X0.374
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.375 X0.376 X0.377 X0.378 X0.379 X0.380 X0.381
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.382 X0.383 X0.384 X0.385 X0.386 X0.387 X0.388
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.389 X0.390 X0.391 X0.392 X249 X253.29 X249.1
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X64.1 X0.393 X0.394 X0.395 X0.396 X0.397 X0.398
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.399 X0.400 X0.401 X0.402 X0.403 X0.404 X0.405
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.406 X0.407 X0.408 X0.409 X0.410 X0.411 X0.412
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.413 X46 X130 X183 X253.30 X253.31 X207
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X2.1 X0.414 X0.415 X0.416 X0.417 X0.418 X0.419
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.420 X0.421 X0.422 X0.423 X0.424 X0.425 X0.426
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.427 X0.428 X0.429 X0.430 X0.431 X0.432 X39.1
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X148 X229 X253.32 X253.33 X253.34 X250 X182.1
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.433 X0.434 X0.435 X0.436 X0.437 X0.438 X0.439
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.440 X0.441 X0.442 X0.443 X0.444 X0.445 X0.446
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.447 X0.448 X0.449 X0.450 X24 X114 X221
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X253.35 X253.36 X253.37 X253.38 X201 X78 X0.451
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.452 X0.453 X0.454 X0.455 X0.456 X0.457 X0.458
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.459 X0.460 X0.461 X0.462 X0.463 X0.464 X0.465
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.466 X0.467 X23 X66 X213 X253.39 X253.40
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X253.41 X253.42 X198.1 X81.1 X2.2 X0.468 X0.469
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.470 X0.471 X0.472 X0.473 X0.474 X0.475 X0.476
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.477 X0.478 X0.479 X0.480 X0.481 X0.482 X0.483
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X18.4 X171 X219.1 X253.43 X253.44 X253.45 X253.46
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X195.1 X80.1 X9 X0.484 X0.485 X0.486 X0.487
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.488 X0.489 X0.490 X0.491 X0.492 X0.493 X0.494
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.495 X0.496 X0.497 X0.498 X0.499 X55 X172.1
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X226 X253.47 X253.48 X253.49 X253.50 X244 X133
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X11.2 X0.500 X0.501 X0.502 X0.503 X0.504 X0.505
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.506 X0.507 X0.508 X0.509 X0.510 X0.511 X0.512
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.513 X0.514 X0.515 X0.516 X0.517 X136.1 X253.51
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X253.52 X253.53 X212 X135 X132 X16.1 X0.518
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.519 X0.520 X0.521 X0.522 X0.523 X0.524 X0.525
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.526 X0.527 X0.528 X0.529 X0.530 X0.531 X0.532
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.533 X0.534 X0.535 X0.536 X0.537 X0.538 X0.539
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.540 X0.541 X0.542 X0.543 X0.544 X0.545 X0.546
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.547 X0.548 X0.549 X0.550 X0.551 X0.552 X0.553
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.554 X0.555 X0.556 X0.557 X0.558 X0.559 X0.560
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.561 X0.562 X0.563 X0.564 X0.565 X0.566 X0.567
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.568 X0.569 X0.570 X0.571 X0.572 X0.573 X0.574
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.575 X0.576 X0.577 X0.578 X0.579 X0.580 X0.581
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.582 X0.583 X0.584 X0.585 X0.586 X0.587 X0.588
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.589 X0.590 X0.591 X0.592 X0.593 X0.594 X0.595
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.596 X0.597 X0.598 X0.599 X0.600 X0.601 X0.602
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.603 X0.604 X0.605 X0.606 X0.607 X0.608 X0.609
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.610 X0.611 X0.612 X0.613 X0.614 X0.615 X0.616
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.617
## "integer"Checking Test Set
Descriptive Analysis of the Test dataset. ( Similar to the traning set )
head(DATASET.test)## X7 X0 X0.1 X0.2 X0.3 X0.4 X0.5 X0.6 X0.7 X0.8 X0.9 X0.10 X0.11 X0.12
## 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 4 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 4 0 0 0 0 0 0 0 0 0 0 0 0 0
## X0.13 X0.14 X0.15 X0.16 X0.17 X0.18 X0.19 X0.20 X0.21 X0.22 X0.23 X0.24
## 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0
## X0.25 X0.26 X0.27 X0.28 X0.29 X0.30 X0.31 X0.32 X0.33 X0.34 X0.35 X0.36
## 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0
## X0.37 X0.38 X0.39 X0.40 X0.41 X0.42 X0.43 X0.44 X0.45 X0.46 X0.47 X0.48
## 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0
## X0.49 X0.50 X0.51 X0.52 X0.53 X0.54 X0.55 X0.56 X0.57 X0.58 X0.59 X0.60
## 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0
## X0.61 X0.62 X0.63 X0.64 X0.65 X0.66 X0.67 X0.68 X0.69 X0.70 X0.71 X0.72
## 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0
## X0.73 X0.74 X0.75 X0.76 X0.77 X0.78 X0.79 X0.80 X0.81 X0.82 X0.83 X0.84
## 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0
## X0.85 X0.86 X0.87 X0.88 X0.89 X0.90 X0.91 X0.92 X0.93 X0.94 X0.95 X0.96
## 1 0 0 0 0 0 0 0 0 0 116 125 171
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0
## X0.97 X0.98 X0.99 X0.100 X0.101 X0.102 X0.103 X0.104 X0.105 X0.106
## 1 255 255 150 93 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.107 X0.108 X0.109 X0.110 X0.111 X0.112 X0.113 X0.114 X0.115 X0.116
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.117 X0.118 X0.119 X0.120 X0.121 X0.122 X0.123 X0.124 X0.125 X0.126
## 1 0 0 0 0 169 253 253 253 253 253
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 11 150 253
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.127 X0.128 X0.129 X0.130 X0.131 X0.132 X0.133 X0.134 X0.135 X0.136
## 1 253 218 30 0 0 0 0 0 0 0
## 2 0 38 254 109 0 0 0 0 0 0
## 3 202 31 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.137 X0.138 X0.139 X0.140 X0.141 X0.142 X0.143 X0.144 X0.145 X0.146
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.147 X0.148 X0.149 X0.150 X0.151 X0.152 X0.153 X0.154 X0.155 X0.156
## 1 0 169 253 253 253 213 142 176 253 253
## 2 0 0 0 0 0 0 0 0 0 87
## 3 0 0 0 0 0 37 251 251 253 107
## 4 0 0 0 50 224 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 77
## 6 0 0 22 192 134 32 0 0 0 0
## X0.157 X0.158 X0.159 X0.160 X0.161 X0.162 X0.163 X0.164 X0.165 X0.166
## 1 122 0 0 0 0 0 0 0 0 0
## 2 252 82 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 70 29 0 0 0 0 0 0
## 5 254 107 3 0 0 0 0 0 0 0
## 6 0 0 0 0 15 77 5 0 0 0
## X0.167 X0.168 X0.169 X0.170 X0.171 X0.172 X0.173 X0.174 X0.175 X0.176
## 1 0 0 0 0 0 0 0 0 52 250
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 17
## X0.177 X0.178 X0.179 X0.180 X0.181 X0.182 X0.183 X0.184 X0.185 X0.186
## 1 253 210 32 12 0 6 206 253 140 0
## 2 0 0 0 0 0 0 0 135 241 0
## 3 0 0 21 197 251 251 253 107 0 0
## 4 0 121 231 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 19 227 254 254
## 6 235 250 169 0 0 0 0 0 0 0
## X0.187 X0.188 X0.189 X0.190 X0.191 X0.192 X0.193 X0.194 X0.195 X0.196
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 148 168 0 0 0 0 0 0 0 0
## 5 9 0 0 0 0 0 0 0 0 0
## 6 0 15 220 241 37 0 0 0 0 0
## X0.197 X0.198 X0.199 X0.200 X0.201 X84 X185 X159 X151 X60 X36 X0.202
## 1 0 0 0 0 0 0 77 251 210 25 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 110 190 251
## 4 0 0 0 0 0 0 0 0 4 195 231 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 20 189 253 147 0 0
## X0.203 X0.204 X0.205 X0.206 X0.207 X0.208 X0.209 X0.210 X0.211 X0.212
## 1 0 122 248 253 65 0 0 0 0 0
## 2 0 0 45 244 150 0 0 0 0 0
## 3 251 251 253 169 109 62 0 0 0 0
## 4 0 0 0 0 0 0 96 210 11 0
## 5 0 0 81 254 254 165 1 0 0 0
## 6 0 0 0 0 0 0 0 139 253 100
## X0.213 X0.214 X0.215 X0.216 X0.217 X0.218 X0.219 X0.220 X0.221 X0.222
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.223 X222 X254 X254.1 X254.2 X254.3 X241 X198 X198.1 X198.2 X198.3
## 1 0 0 0 31 18 0 0 0 0 209 253
## 2 0 0 0 0 0 0 0 0 0 0 84
## 3 0 0 0 0 0 253 251 251 251 251 253
## 4 0 0 0 0 69 252 134 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 7 203
## 6 0 0 70 253 253 21 0 0 0 0 0
## X198.4 X198.5 X198.6 X198.7 X170 X52 X0.224 X0.225 X0.226 X0.227 X0.228
## 1 253 65 0 0 0 0 0 0 0 0 0
## 2 254 63 0 0 0 0 0 0 0 0 0
## 3 251 251 220 51 0 0 0 0 0 0 0
## 4 0 0 0 114 252 21 0 0 0 0 0
## 5 254 254 73 0 0 0 0 0 0 0 0
## 6 0 0 0 43 254 173 13 0 0 0 0
## X0.229 X0.230 X0.231 X0.232 X0.233 X0.234 X0.235 X67 X114 X72 X114.1
## 1 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 182
## 4 0 0 0 0 0 0 0 0 0 45 236
## 5 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 22 153 253 96
## X163 X227 X254.4 X225 X254.5 X254.6 X254.7 X250 X229 X254.8 X254.9 X140
## 1 0 0 0 117 247 253 198 10 0 0 0 0
## 2 0 0 0 0 0 202 223 11 0 0 0 0
## 3 255 253 253 253 253 234 222 253 253 253 0 0
## 4 217 12 0 0 0 0 0 0 0 192 252 21
## 5 0 0 0 0 53 254 254 250 0 0 0 0
## 6 0 0 0 0 0 0 0 0 43 231 254 92
## X0.236 X0.237 X0.238 X0.239 X0.240 X0.241 X0.242 X0.243 X0.244 X0.245
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.246 X0.247 X0.248 X0.249 X0.250 X0.251 X0.252 X17 X66 X14 X67.1 X67.2
## 1 0 0 0 0 0 0 0 0 76 247 253 231
## 2 0 0 0 0 0 0 0 0 0 0 32 254
## 3 0 0 0 0 63 221 253 251 251 251 147 77
## 4 0 0 0 0 168 247 53 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 134 254
## 6 0 0 163 255 204 11 0 0 0 0 0 0
## X67.3 X59 X21 X236 X254.10 X106 X0.253 X0.254 X0.255 X0.256 X0.257
## 1 63 0 0 0 0 0 0 0 0 0 0
## 2 216 0 0 0 0 0 0 0 0 0 0
## 3 62 128 251 251 105 0 0 0 0 0 0
## 4 0 0 18 255 253 21 0 0 0 0 0
## 5 254 180 0 0 0 0 0 0 0 0 0
## 6 0 0 104 254 158 0 0 0 0 0 0
## X0.258 X0.259 X0.260 X0.261 X0.262 X0.263 X0.264 X0.265 X0.266 X0.267
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 32 231
## 4 0 0 0 0 0 0 0 0 84 242
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 162 253 178
## X0.268 X0.269 X0.270 X0.271 X0.272 X0.273 X0.274 X0.275 X0.276 X83 X253
## 1 0 0 0 128 253 253 144 0 0 0 0
## 2 0 0 0 0 0 95 254 195 0 0 0
## 3 251 253 251 220 137 10 0 0 31 230 251
## 4 211 0 0 0 0 0 0 0 0 141 253
## 5 0 0 0 0 0 196 254 248 48 0 0
## 6 5 0 0 0 0 0 0 9 131 237 253
## X209 X18 X0.277 X0.278 X0.279 X0.280 X0.281 X0.282 X0.283 X0.284 X0.285
## 1 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0
## 3 243 113 5 0 0 0 0 0 0 0 0
## 4 189 5 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0
## X0.286 X0.287 X0.288 X0.289 X0.290 X0.291 X0.292 X0.293 X0.294 X0.295
## 1 0 0 0 0 0 0 0 0 176 246
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 37 251 251 253 188 20
## 4 0 0 0 0 169 252 106 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 162 253 253 191 175 70 70
## X0.296 X0.297 X0.298 X0.299 X22 X233 X255 X83.1 X0.300 X0.301 X0.302
## 1 253 159 12 0 0 0 0 0 0 0 0
## 2 0 140 254 77 0 0 0 0 0 0 0
## 3 0 0 0 0 0 109 251 253 251 35 0
## 4 0 0 0 0 32 232 250 66 0 0 0
## 5 58 254 254 237 0 0 0 0 0 0 0
## 6 70 70 133 197 253 253 169 0 0 0 0
## X0.303 X0.304 X0.305 X0.306 X0.307 X0.308 X0.309 X0.310 X0.311 X0.312
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.313 X0.314 X0.315 X0.316 X0.317 X0.318 X0.319 X0.320 X0.321 X0.322
## 1 0 0 0 0 25 234 253 233 35 0
## 2 0 0 0 0 0 0 0 57 237 205
## 3 0 37 251 251 201 30 0 0 0 0
## 4 15 225 252 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 111 254 254
## 6 51 228 253 253 254 253 253 253 253 254
## X0.323 X129 X254.11 X238 X44 X0.324 X0.325 X0.326 X0.327 X0.328 X0.329
## 1 0 0 0 0 0 0 0 0 0 0 0
## 2 8 0 0 0 0 0 0 0 0 0 0
## 3 0 0 31 200 253 251 35 0 0 0 0
## 4 0 134 252 211 0 0 0 0 0 0 0
## 5 132 0 0 0 0 0 0 0 0 0 0
## 6 253 253 219 35 0 0 0 0 0 0 0
## X0.330 X0.331 X0.332 X0.333 X0.334 X0.335 X0.336 X0.337 X0.338 X0.339
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 37 253
## 4 0 0 0 0 0 0 0 22 252 164
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 17 65
## X0.340 X0.341 X0.342 X0.343 X0.344 X0.345 X0.346 X59.1 X249 X254.12 X62
## 1 0 198 253 253 141 0 0 0 0 0 0
## 2 0 0 0 0 124 255 165 0 0 0 0
## 3 253 0 0 0 0 0 0 0 0 32 202
## 4 0 0 0 0 0 0 0 0 169 252 167
## 5 0 0 0 0 163 254 238 28 0 0 0
## 6 137 254 232 137 137 137 44 253 253 161 0
## X0.347 X0.348 X0.349 X0.350 X0.351 X0.352 X0.353 X0.354 X0.355 X0.356
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 255 253 164 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.357 X0.358 X0.359 X0.360 X0.361 X0.362 X0.363 X0.364 X0.365 X0.366
## 1 0 0 0 0 0 0 0 78 248 253
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 140 251 251 0 0
## 4 0 0 0 0 9 204 209 18 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.367 X0.368 X0.369 X0.370 X133 X254.13 X187 X5 X0.371 X0.372 X0.373
## 1 189 12 0 0 0 0 0 0 0 0 0
## 2 0 171 254 81 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 109 251 253 251 35
## 4 0 0 0 0 22 253 253 107 0 0 0
## 5 60 252 254 223 0 0 0 0 0 0 0
## 6 0 0 0 34 254 206 21 0 0 0 0
## X0.374 X0.375 X0.376 X0.377 X0.378 X0.379 X0.380 X0.381 X0.382 X0.383
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.384 X0.385 X0.386 X0.387 X0.388 X0.389 X0.390 X0.391 X0.392 X0.393
## 1 0 0 0 19 200 253 253 141 0 0
## 2 0 0 0 0 0 0 0 24 232 215
## 3 0 0 217 251 251 0 0 0 0 0
## 4 0 0 169 252 199 85 85 85 85 129
## 5 0 0 0 0 0 0 0 79 254 254
## 6 0 0 0 0 0 0 0 0 0 0
## X9 X205 X248 X58 X0.394 X0.395 X0.396 X0.397 X0.398 X0.399 X0.400
## 1 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0
## 3 0 21 63 231 251 253 230 30 0 0 0
## 4 164 195 252 252 106 0 0 0 0 0 0
## 5 154 0 0 0 0 0 0 0 0 0 0
## 6 160 253 69 0 0 0 0 0 0 0 0
## X0.401 X0.402 X0.403 X0.404 X0.405 X0.406 X0.407 X0.408 X0.409 X0.410
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 217
## 4 0 0 0 0 0 0 0 0 0 41
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.411 X0.412 X0.413 X0.414 X0.415 X0.416 X0.417 X126 X254.14 X182
## 1 134 253 253 173 12 0 0 0 0 0
## 2 0 0 0 0 120 254 159 0 0 0
## 3 251 251 0 0 0 0 0 0 144 251
## 4 170 245 252 252 252 252 232 231 251 252
## 5 0 0 0 0 163 254 238 53 0 0
## 6 0 0 0 0 0 0 85 254 241 50
## X0.418 X0.419 X0.420 X0.421 X0.422 X0.423 X0.424 X0.425 X0.426 X0.427
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 251 251 221 61 0 0 0 0 0 0
## 4 252 9 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.428 X0.429 X0.430 X0.431 X0.432 X0.433 X0.434 X0.435 X0.436 X0.437
## 1 0 0 0 0 0 0 0 0 248 253
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 217 251 251
## 4 0 0 0 0 0 0 0 0 0 49
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.438 X0.439 X0.440 X0.441 X75 X251 X240 X57 X0.442 X0.443 X0.444
## 1 253 25 0 0 0 0 0 0 0 0 0
## 2 0 0 151 254 142 0 0 0 0 0 0
## 3 0 0 0 0 0 182 221 251 251 251 180
## 4 84 84 84 84 0 0 161 252 252 0 0
## 5 0 28 252 254 210 0 0 0 0 0 0
## 6 0 0 0 0 158 254 165 0 0 0 0
## X0.445 X0.446 X0.447 X0.448 X0.449 X0.450 X0.451 X0.452 X0.453 X0.454
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.455 X0.456 X0.457 X0.458 X0.459 X0.460 X0.461 X0.462 X0.463 X0.464
## 1 0 0 0 0 0 248 253 253 43 20
## 2 0 0 0 0 0 0 0 0 0 228
## 3 0 0 0 0 218 253 253 73 73 228
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 86 254
## 6 0 0 0 0 0 0 0 0 0 0
## X19 X221 X254.15 X166 X0.465 X0.466 X0.467 X0.468 X0.469 X0.470 X0.471
## 1 20 20 20 5 0 5 20 20 37 150 150
## 2 254 66 0 0 0 0 0 0 0 0 0
## 3 253 253 255 253 253 253 253 0 0 0 0
## 4 0 0 0 127 252 252 45 0 0 0 0
## 5 254 131 0 0 0 0 0 0 0 0 0
## 6 0 231 244 50 0 0 0 0 0 0 0
## X0.472 X0.473 X0.474 X0.475 X0.476 X0.477 X0.478 X0.479 X0.480 X0.481
## 1 150 147 10 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.482 X0.483 X0.484 X0.485 X0.486 X0.487 X3 X203 X254.16 X219 X35
## 1 0 0 248 253 253 253 253 253 253 253 168
## 2 0 0 0 0 0 61 251 254 66 0 0
## 3 0 113 251 251 253 251 251 251 251 253 251
## 4 0 0 0 0 0 0 0 0 0 0 128
## 5 0 0 0 0 0 105 254 234 20 0 0
## 6 0 0 0 0 0 0 0 104 254 232 0
## X0.488 X0.489 X0.490 X0.491 X0.492 X0.493 X0.494 X0.495 X0.496 X0.497
## 1 143 166 253 253 253 253 253 253 253 123
## 2 0 0 0 0 0 0 0 0 0 0
## 3 251 251 147 0 0 0 0 0 0 0
## 4 253 253 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.498 X0.499 X0.500 X0.501 X0.502 X0.503 X0.504 X0.505 X0.506 X0.507
## 1 0 0 0 0 0 0 0 0 0 174
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 31 230
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.508 X0.509 X0.510 X38 X254.17 X254.18 X77 X0.511 X0.512 X0.513 X0.514
## 1 253 253 253 253 253 253 253 253 253 253 253
## 2 0 0 141 254 205 3 0 0 0 0 0
## 3 251 253 251 251 251 251 253 230 189 35 10
## 4 0 0 0 0 0 0 0 127 252 252 0
## 5 0 0 175 254 204 5 0 0 0 0 0
## 6 0 0 0 0 208 253 157 0 13 30 0
## X0.515 X0.516 X0.517 X0.518 X0.519 X0.520 X0.521 X0.522 X0.523 X0.524
## 1 249 247 247 169 117 117 57 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.525 X0.526 X0.527 X0.528 X0.529 X0.530 X0.531 X0.532 X0.533 X31 X224
## 1 0 0 0 0 0 0 0 118 123 123 123
## 2 0 0 0 0 0 0 0 0 10 215 254
## 3 0 0 0 0 0 0 62 142 253 251 251
## 4 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 5 211 254
## 6 0 0 0 0 0 0 0 0 0 0 0
## X254.19 X115 X1 X0.534 X0.535 X0.536 X0.537 X0.538 X0.539 X0.540 X0.541
## 1 166 253 253 253 155 123 123 41 0 0 0
## 2 121 0 0 0 0 0 0 0 0 0 0
## 3 251 251 253 107 0 0 0 0 0 0 0
## 4 0 0 0 135 252 244 0 0 0 0 0
## 5 196 0 0 0 0 0 0 0 0 0 0
## 6 208 253 154 91 204 161 0 0 0 0 0
## X0.542 X0.543 X0.544 X0.545 X0.546 X0.547 X0.548 X0.549 X0.550 X0.551
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.552 X0.553 X0.554 X0.555 X0.556 X133.1 X254.20 X254.21 X52.1 X0.557
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 5 198 176 10 0 0
## 3 0 0 0 0 72 174 251 173 71 72
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 3 158 254 160 0 0
## 6 0 0 0 0 0 0 0 208 253 254
## X0.558 X0.559 X0.560 X0.561 X0.562 X0.563 X0.564 X0.565 X0.566 X0.567
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 30 0 0 0 0 0 0 0 0 0
## 4 232 236 111 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 253 154 29 0 0 0 0 0 0 0
## X0.568 X0.569 X0.570 X0.571 X0.572 X0.573 X0.574 X0.575 X0.576 X0.577
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.578 X0.579 X61 X242 X254.22 X254.23 X52.2 X0.580 X0.581 X0.582 X0.583
## 1 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 179 66 0
## 5 0 0 0 26 157 107 0 0 0 0 0
## 6 0 0 0 0 0 61 190 128 23 6 0
## X0.584 X0.585 X0.586 X0.587 X0.588 X0.589 X0.590 X0.591 X0.592 X0.593
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.594 X0.595 X0.596 X0.597 X0.598 X0.599 X0.600 X0.601 X0.602 X121
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X254.24 X254.25 X219.1 X40 X0.603 X0.604 X0.605 X0.606 X0.607 X0.608
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.609 X0.610 X0.611 X0.612 X0.613 X0.614 X0.615 X0.616 X0.617 X0.618
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.619 X0.620 X0.621 X0.622 X0.623 X0.624 X0.625 X121.1 X254.26 X207
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X18.1 X0.626 X0.627 X0.628 X0.629 X0.630 X0.631 X0.632 X0.633 X0.634
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.635 X0.636 X0.637 X0.638 X0.639 X0.640 X0.641 X0.642 X0.643 X0.644
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.645 X0.646 X0.647 X0.648 X0.649 X0.650 X0.651 X0.652 X0.653 X0.654
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.655 X0.656 X0.657 X0.658 X0.659 X0.660 X0.661 X0.662 X0.663 X0.664
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## X0.665 X0.666 X0.667
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0dim(DATASET.test)## [1] 9999 785nrow(DATASET.test)## [1] 9999ncol(DATASET.test)## [1] 785colnames(DATASET.test)## [1] "X7" "X0" "X0.1" "X0.2" "X0.3" "X0.4" "X0.5"
## [8] "X0.6" "X0.7" "X0.8" "X0.9" "X0.10" "X0.11" "X0.12"
## [15] "X0.13" "X0.14" "X0.15" "X0.16" "X0.17" "X0.18" "X0.19"
## [22] "X0.20" "X0.21" "X0.22" "X0.23" "X0.24" "X0.25" "X0.26"
## [29] "X0.27" "X0.28" "X0.29" "X0.30" "X0.31" "X0.32" "X0.33"
## [36] "X0.34" "X0.35" "X0.36" "X0.37" "X0.38" "X0.39" "X0.40"
## [43] "X0.41" "X0.42" "X0.43" "X0.44" "X0.45" "X0.46" "X0.47"
## [50] "X0.48" "X0.49" "X0.50" "X0.51" "X0.52" "X0.53" "X0.54"
## [57] "X0.55" "X0.56" "X0.57" "X0.58" "X0.59" "X0.60" "X0.61"
## [64] "X0.62" "X0.63" "X0.64" "X0.65" "X0.66" "X0.67" "X0.68"
## [71] "X0.69" "X0.70" "X0.71" "X0.72" "X0.73" "X0.74" "X0.75"
## [78] "X0.76" "X0.77" "X0.78" "X0.79" "X0.80" "X0.81" "X0.82"
## [85] "X0.83" "X0.84" "X0.85" "X0.86" "X0.87" "X0.88" "X0.89"
## [92] "X0.90" "X0.91" "X0.92" "X0.93" "X0.94" "X0.95" "X0.96"
## [99] "X0.97" "X0.98" "X0.99" "X0.100" "X0.101" "X0.102" "X0.103"
## [106] "X0.104" "X0.105" "X0.106" "X0.107" "X0.108" "X0.109" "X0.110"
## [113] "X0.111" "X0.112" "X0.113" "X0.114" "X0.115" "X0.116" "X0.117"
## [120] "X0.118" "X0.119" "X0.120" "X0.121" "X0.122" "X0.123" "X0.124"
## [127] "X0.125" "X0.126" "X0.127" "X0.128" "X0.129" "X0.130" "X0.131"
## [134] "X0.132" "X0.133" "X0.134" "X0.135" "X0.136" "X0.137" "X0.138"
## [141] "X0.139" "X0.140" "X0.141" "X0.142" "X0.143" "X0.144" "X0.145"
## [148] "X0.146" "X0.147" "X0.148" "X0.149" "X0.150" "X0.151" "X0.152"
## [155] "X0.153" "X0.154" "X0.155" "X0.156" "X0.157" "X0.158" "X0.159"
## [162] "X0.160" "X0.161" "X0.162" "X0.163" "X0.164" "X0.165" "X0.166"
## [169] "X0.167" "X0.168" "X0.169" "X0.170" "X0.171" "X0.172" "X0.173"
## [176] "X0.174" "X0.175" "X0.176" "X0.177" "X0.178" "X0.179" "X0.180"
## [183] "X0.181" "X0.182" "X0.183" "X0.184" "X0.185" "X0.186" "X0.187"
## [190] "X0.188" "X0.189" "X0.190" "X0.191" "X0.192" "X0.193" "X0.194"
## [197] "X0.195" "X0.196" "X0.197" "X0.198" "X0.199" "X0.200" "X0.201"
## [204] "X84" "X185" "X159" "X151" "X60" "X36" "X0.202"
## [211] "X0.203" "X0.204" "X0.205" "X0.206" "X0.207" "X0.208" "X0.209"
## [218] "X0.210" "X0.211" "X0.212" "X0.213" "X0.214" "X0.215" "X0.216"
## [225] "X0.217" "X0.218" "X0.219" "X0.220" "X0.221" "X0.222" "X0.223"
## [232] "X222" "X254" "X254.1" "X254.2" "X254.3" "X241" "X198"
## [239] "X198.1" "X198.2" "X198.3" "X198.4" "X198.5" "X198.6" "X198.7"
## [246] "X170" "X52" "X0.224" "X0.225" "X0.226" "X0.227" "X0.228"
## [253] "X0.229" "X0.230" "X0.231" "X0.232" "X0.233" "X0.234" "X0.235"
## [260] "X67" "X114" "X72" "X114.1" "X163" "X227" "X254.4"
## [267] "X225" "X254.5" "X254.6" "X254.7" "X250" "X229" "X254.8"
## [274] "X254.9" "X140" "X0.236" "X0.237" "X0.238" "X0.239" "X0.240"
## [281] "X0.241" "X0.242" "X0.243" "X0.244" "X0.245" "X0.246" "X0.247"
## [288] "X0.248" "X0.249" "X0.250" "X0.251" "X0.252" "X17" "X66"
## [295] "X14" "X67.1" "X67.2" "X67.3" "X59" "X21" "X236"
## [302] "X254.10" "X106" "X0.253" "X0.254" "X0.255" "X0.256" "X0.257"
## [309] "X0.258" "X0.259" "X0.260" "X0.261" "X0.262" "X0.263" "X0.264"
## [316] "X0.265" "X0.266" "X0.267" "X0.268" "X0.269" "X0.270" "X0.271"
## [323] "X0.272" "X0.273" "X0.274" "X0.275" "X0.276" "X83" "X253"
## [330] "X209" "X18" "X0.277" "X0.278" "X0.279" "X0.280" "X0.281"
## [337] "X0.282" "X0.283" "X0.284" "X0.285" "X0.286" "X0.287" "X0.288"
## [344] "X0.289" "X0.290" "X0.291" "X0.292" "X0.293" "X0.294" "X0.295"
## [351] "X0.296" "X0.297" "X0.298" "X0.299" "X22" "X233" "X255"
## [358] "X83.1" "X0.300" "X0.301" "X0.302" "X0.303" "X0.304" "X0.305"
## [365] "X0.306" "X0.307" "X0.308" "X0.309" "X0.310" "X0.311" "X0.312"
## [372] "X0.313" "X0.314" "X0.315" "X0.316" "X0.317" "X0.318" "X0.319"
## [379] "X0.320" "X0.321" "X0.322" "X0.323" "X129" "X254.11" "X238"
## [386] "X44" "X0.324" "X0.325" "X0.326" "X0.327" "X0.328" "X0.329"
## [393] "X0.330" "X0.331" "X0.332" "X0.333" "X0.334" "X0.335" "X0.336"
## [400] "X0.337" "X0.338" "X0.339" "X0.340" "X0.341" "X0.342" "X0.343"
## [407] "X0.344" "X0.345" "X0.346" "X59.1" "X249" "X254.12" "X62"
## [414] "X0.347" "X0.348" "X0.349" "X0.350" "X0.351" "X0.352" "X0.353"
## [421] "X0.354" "X0.355" "X0.356" "X0.357" "X0.358" "X0.359" "X0.360"
## [428] "X0.361" "X0.362" "X0.363" "X0.364" "X0.365" "X0.366" "X0.367"
## [435] "X0.368" "X0.369" "X0.370" "X133" "X254.13" "X187" "X5"
## [442] "X0.371" "X0.372" "X0.373" "X0.374" "X0.375" "X0.376" "X0.377"
## [449] "X0.378" "X0.379" "X0.380" "X0.381" "X0.382" "X0.383" "X0.384"
## [456] "X0.385" "X0.386" "X0.387" "X0.388" "X0.389" "X0.390" "X0.391"
## [463] "X0.392" "X0.393" "X9" "X205" "X248" "X58" "X0.394"
## [470] "X0.395" "X0.396" "X0.397" "X0.398" "X0.399" "X0.400" "X0.401"
## [477] "X0.402" "X0.403" "X0.404" "X0.405" "X0.406" "X0.407" "X0.408"
## [484] "X0.409" "X0.410" "X0.411" "X0.412" "X0.413" "X0.414" "X0.415"
## [491] "X0.416" "X0.417" "X126" "X254.14" "X182" "X0.418" "X0.419"
## [498] "X0.420" "X0.421" "X0.422" "X0.423" "X0.424" "X0.425" "X0.426"
## [505] "X0.427" "X0.428" "X0.429" "X0.430" "X0.431" "X0.432" "X0.433"
## [512] "X0.434" "X0.435" "X0.436" "X0.437" "X0.438" "X0.439" "X0.440"
## [519] "X0.441" "X75" "X251" "X240" "X57" "X0.442" "X0.443"
## [526] "X0.444" "X0.445" "X0.446" "X0.447" "X0.448" "X0.449" "X0.450"
## [533] "X0.451" "X0.452" "X0.453" "X0.454" "X0.455" "X0.456" "X0.457"
## [540] "X0.458" "X0.459" "X0.460" "X0.461" "X0.462" "X0.463" "X0.464"
## [547] "X19" "X221" "X254.15" "X166" "X0.465" "X0.466" "X0.467"
## [554] "X0.468" "X0.469" "X0.470" "X0.471" "X0.472" "X0.473" "X0.474"
## [561] "X0.475" "X0.476" "X0.477" "X0.478" "X0.479" "X0.480" "X0.481"
## [568] "X0.482" "X0.483" "X0.484" "X0.485" "X0.486" "X0.487" "X3"
## [575] "X203" "X254.16" "X219" "X35" "X0.488" "X0.489" "X0.490"
## [582] "X0.491" "X0.492" "X0.493" "X0.494" "X0.495" "X0.496" "X0.497"
## [589] "X0.498" "X0.499" "X0.500" "X0.501" "X0.502" "X0.503" "X0.504"
## [596] "X0.505" "X0.506" "X0.507" "X0.508" "X0.509" "X0.510" "X38"
## [603] "X254.17" "X254.18" "X77" "X0.511" "X0.512" "X0.513" "X0.514"
## [610] "X0.515" "X0.516" "X0.517" "X0.518" "X0.519" "X0.520" "X0.521"
## [617] "X0.522" "X0.523" "X0.524" "X0.525" "X0.526" "X0.527" "X0.528"
## [624] "X0.529" "X0.530" "X0.531" "X0.532" "X0.533" "X31" "X224"
## [631] "X254.19" "X115" "X1" "X0.534" "X0.535" "X0.536" "X0.537"
## [638] "X0.538" "X0.539" "X0.540" "X0.541" "X0.542" "X0.543" "X0.544"
## [645] "X0.545" "X0.546" "X0.547" "X0.548" "X0.549" "X0.550" "X0.551"
## [652] "X0.552" "X0.553" "X0.554" "X0.555" "X0.556" "X133.1" "X254.20"
## [659] "X254.21" "X52.1" "X0.557" "X0.558" "X0.559" "X0.560" "X0.561"
## [666] "X0.562" "X0.563" "X0.564" "X0.565" "X0.566" "X0.567" "X0.568"
## [673] "X0.569" "X0.570" "X0.571" "X0.572" "X0.573" "X0.574" "X0.575"
## [680] "X0.576" "X0.577" "X0.578" "X0.579" "X61" "X242" "X254.22"
## [687] "X254.23" "X52.2" "X0.580" "X0.581" "X0.582" "X0.583" "X0.584"
## [694] "X0.585" "X0.586" "X0.587" "X0.588" "X0.589" "X0.590" "X0.591"
## [701] "X0.592" "X0.593" "X0.594" "X0.595" "X0.596" "X0.597" "X0.598"
## [708] "X0.599" "X0.600" "X0.601" "X0.602" "X121" "X254.24" "X254.25"
## [715] "X219.1" "X40" "X0.603" "X0.604" "X0.605" "X0.606" "X0.607"
## [722] "X0.608" "X0.609" "X0.610" "X0.611" "X0.612" "X0.613" "X0.614"
## [729] "X0.615" "X0.616" "X0.617" "X0.618" "X0.619" "X0.620" "X0.621"
## [736] "X0.622" "X0.623" "X0.624" "X0.625" "X121.1" "X254.26" "X207"
## [743] "X18.1" "X0.626" "X0.627" "X0.628" "X0.629" "X0.630" "X0.631"
## [750] "X0.632" "X0.633" "X0.634" "X0.635" "X0.636" "X0.637" "X0.638"
## [757] "X0.639" "X0.640" "X0.641" "X0.642" "X0.643" "X0.644" "X0.645"
## [764] "X0.646" "X0.647" "X0.648" "X0.649" "X0.650" "X0.651" "X0.652"
## [771] "X0.653" "X0.654" "X0.655" "X0.656" "X0.657" "X0.658" "X0.659"
## [778] "X0.660" "X0.661" "X0.662" "X0.663" "X0.664" "X0.665" "X0.666"
## [785] "X0.667"summary(DATASET.test)## X7 X0 X0.1 X0.2 X0.3
## Min. :0.000 Min. :0 Min. :0 Min. :0 Min. :0
## 1st Qu.:2.000 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0
## Median :4.000 Median :0 Median :0 Median :0 Median :0
## Mean :4.443 Mean :0 Mean :0 Mean :0 Mean :0
## 3rd Qu.:7.000 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
## Max. :9.000 Max. :0 Max. :0 Max. :0 Max. :0
## X0.4 X0.5 X0.6 X0.7 X0.8 X0.9
## Min. :0 Min. :0 Min. :0 Min. :0 Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0
## Median :0 Median :0 Median :0 Median :0 Median :0 Median :0
## Mean :0 Mean :0 Mean :0 Mean :0 Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0 Max. :0 Max. :0 Max. :0 Max. :0
## X0.10 X0.11 X0.12 X0.13 X0.14 X0.15
## Min. :0 Min. :0 Min. :0 Min. :0 Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0
## Median :0 Median :0 Median :0 Median :0 Median :0 Median :0
## Mean :0 Mean :0 Mean :0 Mean :0 Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0 Max. :0 Max. :0 Max. :0 Max. :0
## X0.16 X0.17 X0.18 X0.19 X0.20 X0.21
## Min. :0 Min. :0 Min. :0 Min. :0 Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0
## Median :0 Median :0 Median :0 Median :0 Median :0 Median :0
## Mean :0 Mean :0 Mean :0 Mean :0 Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0 Max. :0 Max. :0 Max. :0 Max. :0
## X0.22 X0.23 X0.24 X0.25 X0.26 X0.27
## Min. :0 Min. :0 Min. :0 Min. :0 Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0
## Median :0 Median :0 Median :0 Median :0 Median :0 Median :0
## Mean :0 Mean :0 Mean :0 Mean :0 Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0 Max. :0 Max. :0 Max. :0 Max. :0
## X0.28 X0.29 X0.30 X0.31 X0.32
## Min. :0 Min. :0 Min. :0 Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0
## Median :0 Median :0 Median :0 Median :0 Median :0
## Mean :0 Mean :0 Mean :0 Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0 Max. :0 Max. :0 Max. :0
## X0.33 X0.34 X0.35
## Min. : 0.0000 Min. : 0.0000 Min. : 0.0000
## 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.0000
## Median : 0.0000 Median : 0.0000 Median : 0.0000
## Mean : 0.0038 Mean : 0.0236 Mean : 0.0158
## 3rd Qu.: 0.0000 3rd Qu.: 0.0000 3rd Qu.: 0.0000
## Max. :38.0000 Max. :236.0000 Max. :158.0000
## X0.36 X0.37 X0.38
## Min. : 0.0000 Min. : 0.00000 Min. : 0.0000
## 1st Qu.: 0.0000 1st Qu.: 0.00000 1st Qu.: 0.0000
## Median : 0.0000 Median : 0.00000 Median : 0.0000
## Mean : 0.0403 Mean : 0.05111 Mean : 0.1047
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## Median : 0.0000 Median : 0.0000 Median : 0.0000
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## Median : 0.00 Median : 0.0 Median : 0.00 Median : 0.000
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## Median : 0.00000 Median :0 Median :0 Median : 0.0000
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## Median : 0.00 Median : 0.000 Median : 0.000 Median : 0.0000
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## Max. :254.0000 Max. :255.000 Max. :255.000 Max. :255.000
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## Median : 0.00 Median : 0.000 Median : 0.000 Median : 0.0000
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## Median : 0.0000 Median :0 Median : 0.0000 Median : 0.0000
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## Max. :255.00 Max. :255.0 Max. :255 Max. :255.0
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## Median : 0.00 Median : 0.00 Median : 0.000 Median : 0.000
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## Median : 0.0000 Median :0 Median : 0.00000 Median : 0.0000
## Mean : 0.0285 Mean :0 Mean : 0.06561 Mean : 0.3549
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## Max. :146.0000 Max. :0 Max. :255.00000 Max. :255.0000
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## 1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.00
## Median : 0.000 Median : 0.00 Median : 0.000 Median : 0.00
## Mean : 1.206 Mean : 3.72 Mean : 8.228 Mean : 15.87
## 3rd Qu.: 0.000 3rd Qu.: 0.00 3rd Qu.: 0.000 3rd Qu.: 0.00
## Max. :255.000 Max. :255.00 Max. :255.000 Max. :255.00
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## Median : 0.00 Median : 0.00 Median : 0.00 Median : 32.00
## Mean : 28.98 Mean : 49.37 Mean : 73.68 Mean : 98.02
## 3rd Qu.: 0.00 3rd Qu.: 50.00 3rd Qu.:170.00 3rd Qu.:234.00
## Max. :255.00 Max. :255.00 Max. :255.00 Max. :255.00
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## Median : 86.0 Median :117 Median :121.0 Median :118.0
## Mean :114.6 Mean :122 Mean :123.5 Mean :123.3
## 3rd Qu.:252.0 3rd Qu.:252 3rd Qu.:252.0 3rd Qu.:252.0
## Max. :255.0 Max. :255 Max. :255.0 Max. :255.0
## X198.3 X198.4 X198.5 X198.6
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## Median :117.0 Median :121.0 Median :125 Median :102.0
## Mean :122.4 Mean :123.1 Mean :124 Mean :117.5
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## Max. :255.0 Max. :255.0 Max. :255 Max. :255.0
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## Median : 36.0 Median : 0.00 Median : 0.00 Median : 0.00
## Mean :100.5 Mean : 75.03 Mean : 49.46 Mean : 28.82
## 3rd Qu.:240.0 3rd Qu.:180.00 3rd Qu.: 49.00 3rd Qu.: 0.00
## Max. :255.0 Max. :255.00 Max. :255.00 Max. :255.00
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## Median : 0.00 Median : 0.000 Median : 0.00 Median : 0.0000
## Mean : 14.79 Mean : 6.968 Mean : 2.81 Mean : 0.7285
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## Max. :255.00 Max. :255.000 Max. :255.00 Max. :248.0000
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## Median : 0.0000 Median : 0.0000 Median : 0.0000
## Mean : 0.0462 Mean : 0.0159 Mean : 0.1574
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## Median : 0.0000 Median : 0.000 Median : 0.000 Median : 0.000
## Mean : 0.4923 Mean : 1.661 Mean : 4.267 Mean : 9.218
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## Max. :255.0000 Max. :255.000 Max. :255.000 Max. :255.000
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## Median : 0.00 Median : 0.00 Median : 0.00 Median : 0.00
## Mean : 18.28 Mean : 33.71 Mean : 58.01 Mean : 86.21
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## Max. :255.00 Max. :255.00 Max. :255.00 Max. :255.00
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## Median : 58.0 Median : 87.0 Median : 70 Median : 42.0
## Mean :107.8 Mean :114.8 Mean :110 Mean :101.7
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## Max. :255.0 Max. :255.0 Max. :255 Max. :255.0
## X254.5 X254.6 X254.7 X250
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## Median : 31.00 Median : 39.00 Median : 58 Median : 84.0
## Mean : 98.69 Mean : 99.29 Mean :105 Mean :113.5
## 3rd Qu.:239.00 3rd Qu.:233.00 3rd Qu.:241 3rd Qu.:252.0
## Max. :255.00 Max. :255.00 Max. :255 Max. :255.0
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## Median : 79.0 Median : 29.00 Median : 0.00 Median : 0.00
## Mean :113.2 Mean : 98.71 Mean : 74.64 Mean : 49.39
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## Max. :255.0 Max. :255.00 Max. :255.00 Max. :255.00
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## Median : 0.00 Median : 0.00 Median : 0.000 Median : 0.000
## Mean : 28.98 Mean : 14.32 Mean : 6.017 Mean : 2.041
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## Median : 0.0000 Median : 0.00000 Median : 0.0000
## Mean : 0.5271 Mean : 0.05521 Mean : 0.0283
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## Median : 0.0000 Median : 0.0000 Median : 0.000 Median : 0.00
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## Median : 0.000 Median : 0.00 Median : 0.0 Median : 0.00
## Mean : 8.727 Mean : 18.97 Mean : 36.8 Mean : 63.89
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## Max. :255.000 Max. :255.00 Max. :255.0 Max. :255.00
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## Median : 9.0 Median : 57.0 Median : 50.0 Median : 14.00
## Mean : 92.1 Mean :107.8 Mean :104.9 Mean : 90.26
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## Max. :255.0 Max. :255.0 Max. :255.0 Max. :255.00
## X14 X67.1 X67.2 X67.3
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## Median : 0.00 Median : 0.0 Median : 6.00 Median : 28.00
## Mean : 80.31 Mean : 79.8 Mean : 84.55 Mean : 94.49
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## Max. :255.00 Max. :255.0 Max. :255.00 Max. :255.00
## X59 X21 X236 X254.10
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## Median : 64.0 Median : 66 Median : 15.00 Median : 0.00
## Mean :107.8 Mean :109 Mean : 93.75 Mean : 69.66
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## Max. :255.0 Max. :255 Max. :255.00 Max. :255.00
## X106 X0.253 X0.254 X0.255
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## Median : 0.00 Median : 0.00 Median : 0.00 Median : 0.000
## Mean : 45.43 Mean : 26.93 Mean : 13.11 Mean : 4.785
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## Max. :255.00 Max. :255.00 Max. :255.00 Max. :255.000
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## Median : 0.000 Median : 0.0000 Median : 0.00000
## Mean : 1.099 Mean : 0.3493 Mean : 0.06621
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## Max. :253.000 Max. :253.0000 Max. :243.00000
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## 1st Qu.: 0.0000 1st Qu.: 0.00000 1st Qu.: 0.0000
## Median : 0.0000 Median : 0.00000 Median : 0.0000
## Mean : 0.0038 Mean : 0.06231 Mean : 0.3486
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## Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.00
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## Mean : 40.82 Mean : 69.52 Mean : 95.18 Mean :104.5
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## Max. :255.00 Max. :255.00 Max. :255.00 Max. :255.0
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## Median : 19.0 Median : 0.00 Median : 0.00 Median : 0.00
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## Max. :255.0 Max. :255.00 Max. :255.00 Max. :255.00
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## Median : 3.00 Median : 38.00 Median : 74.0 Median : 64.0
## Mean : 86.81 Mean : 99.05 Mean :110.5 Mean :107.5
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## Max. :255.00 Max. :255.00 Max. :255.0 Max. :255.0
## X253 X209 X18 X0.277
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## Median : 3.00 Median : 0.00 Median : 0.00 Median : 0.00
## Mean : 88.45 Mean : 63.51 Mean : 40.99 Mean : 24.69
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## Max. :255.00 Max. :255.00 Max. :255.00 Max. :255.00
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## Median : 0.00 Median : 0.00 Median : 0.0000 Median : 0.0000
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## Median : 0.0000 Median :0 Median : 0.0000 Median : 0.0000
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## Max. :156.0000 Max. :0 Max. :253.0000 Max. :255.0000
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## Median : 0.0000 Median : 0.000 Median : 0.000 Median : 0.0
## Mean : 0.6347 Mean : 2.419 Mean : 8.813 Mean : 22.8
## 3rd Qu.: 0.0000 3rd Qu.: 0.000 3rd Qu.: 0.000 3rd Qu.: 0.0
## Max. :255.0000 Max. :255.000 Max. :255.000 Max. :255.0
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## Median : 0.00 Median : 0.00 Median : 11.00 Median : 29.0
## Mean : 45.78 Mean : 74.17 Mean : 95.35 Mean :100.8
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## Max. :255.00 Max. :255.00 Max. :255.00 Max. :255.0
## X0.294 X0.295 X0.296 X0.297
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## Median : 7.00 Median : 0.0 Median : 0.00 Median : 2.00
## Mean : 89.76 Mean : 77.4 Mean : 78.65 Mean : 90.29
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## Max. :255.00 Max. :255.0 Max. :255.00 Max. :255.00
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## Median : 42.0 Median : 92.0 Median :110.0 Median : 66
## Mean :103.8 Mean :115.3 Mean :120.2 Mean :109
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## Max. :255.0 Max. :255.0 Max. :255.0 Max. :255
## X255 X83.1 X0.300 X0.301
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## Median : 0.00 Median : 0.00 Median : 0.00 Median : 0.00
## Mean : 83.88 Mean : 57.95 Mean : 37.56 Mean : 23.94
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## Max. :255.00 Max. :255.00 Max. :255.00 Max. :255.00
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## Median : 0 Median : 0.00 Median : 0.0000 Median : 0.0000
## Mean : 13 Mean : 4.75 Mean : 0.5852 Mean : 0.0385
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## Median :0 Median :0 Median : 0.0000 Median : 0.0000
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## Median : 0.0000 Median : 0.000 Median : 0.00 Median : 0.00
## Mean : 0.4556 Mean : 2.344 Mean : 10.24 Mean : 25.96
## 3rd Qu.: 0.0000 3rd Qu.: 0.000 3rd Qu.: 0.00 3rd Qu.: 0.00
## Max. :255.0000 Max. :255.000 Max. :255.00 Max. :255.00
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## Median : 0.00 Median : 0.00 Median : 4.00 Median : 20.00
## Mean : 49.68 Mean : 76.05 Mean : 93.78 Mean : 97.01
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## Max. :255.00 Max. :255.00 Max. :255.00 Max. :255.00
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## Median : 4.00 Median : 2.00 Median : 25.00 Median : 72.0
## Mean : 89.48 Mean : 84.42 Mean : 96.38 Mean :113.7
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## Max. :255.00 Max. :255.00 Max. :255.00 Max. :255.0
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## Median :132.0 Median :149.0 Median :140 Median : 67
## Mean :127.3 Mean :132.8 Mean :129 Mean :110
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## Max. :255.0 Max. :255.0 Max. :255 Max. :255
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## Median : 0.0 Median : 0.00 Median : 0.0 Median : 0.00
## Mean : 80.9 Mean : 55.46 Mean : 37.3 Mean : 24.69
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## Max. :255.0 Max. :255.00 Max. :255.0 Max. :255.00
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## Median : 0.00 Median : 0.000 Median : 0.0000 Median : 0.0000
## Mean : 13.85 Mean : 5.178 Mean : 0.6202 Mean : 0.0265
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## Median : 0.000 Median : 0.00 Median : 0.00 Median : 0.00
## Mean : 2.579 Mean : 12.04 Mean : 28.16 Mean : 51.46
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## Max. :255.000 Max. :255.00 Max. :255.00 Max. :255.00
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## Median : 0.00 Median : 0.0 Median : 11.00 Median : 12.00
## Mean : 75.21 Mean : 90.8 Mean : 93.71 Mean : 91.57
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## Max. :255.00 Max. :255.0 Max. :255.00 Max. :255.00
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## Median : 32.00 Median : 96.0 Median :155.0 Median :183.0
## Mean : 95.76 Mean :116.3 Mean :132.7 Mean :142.8
## 3rd Qu.:229.00 3rd Qu.:252.0 3rd Qu.:253.0 3rd Qu.:253.0
## Max. :255.00 Max. :255.0 Max. :255.0 Max. :255.0
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## Mean :102.7 Mean :114.8 Mean :124.2 Mean :133.4
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## Max. :255 Max. :255.0 Max. :255.0 Max. :255.00
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## Median : 0.00 Median : 0.00 Median : 0.00 Median : 0.00
## Mean : 69.49 Mean : 45.68 Mean : 27.43 Mean : 14.73
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## Max. :255.00 Max. :255.00 Max. :255.00 Max. :255.00
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## Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.0000
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## Median : 0.0000 Median : 0.000 Median : 0.000 Median : 0.00
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## Max. :255.00 Max. :255.0 Max. :255.0 Max. :255.0
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## Median : 0.0000 Median :0 Median :0 Median : 0.0000
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## Min. :0 Min. :0 Min. :0 Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0
## Median :0 Median :0 Median :0 Median :0 Median :0
## Mean :0 Mean :0 Mean :0 Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0 Max. :0 Max. :0 Max. :0sapply(DATASET.test[1, ], class)## X7 X0 X0.1 X0.2 X0.3 X0.4 X0.5
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.6 X0.7 X0.8 X0.9 X0.10 X0.11 X0.12
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.13 X0.14 X0.15 X0.16 X0.17 X0.18 X0.19
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.20 X0.21 X0.22 X0.23 X0.24 X0.25 X0.26
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.27 X0.28 X0.29 X0.30 X0.31 X0.32 X0.33
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.34 X0.35 X0.36 X0.37 X0.38 X0.39 X0.40
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.41 X0.42 X0.43 X0.44 X0.45 X0.46 X0.47
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.48 X0.49 X0.50 X0.51 X0.52 X0.53 X0.54
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.55 X0.56 X0.57 X0.58 X0.59 X0.60 X0.61
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.62 X0.63 X0.64 X0.65 X0.66 X0.67 X0.68
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.69 X0.70 X0.71 X0.72 X0.73 X0.74 X0.75
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.76 X0.77 X0.78 X0.79 X0.80 X0.81 X0.82
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.83 X0.84 X0.85 X0.86 X0.87 X0.88 X0.89
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.90 X0.91 X0.92 X0.93 X0.94 X0.95 X0.96
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.97 X0.98 X0.99 X0.100 X0.101 X0.102 X0.103
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.104 X0.105 X0.106 X0.107 X0.108 X0.109 X0.110
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.111 X0.112 X0.113 X0.114 X0.115 X0.116 X0.117
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.118 X0.119 X0.120 X0.121 X0.122 X0.123 X0.124
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.125 X0.126 X0.127 X0.128 X0.129 X0.130 X0.131
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.132 X0.133 X0.134 X0.135 X0.136 X0.137 X0.138
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.139 X0.140 X0.141 X0.142 X0.143 X0.144 X0.145
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.146 X0.147 X0.148 X0.149 X0.150 X0.151 X0.152
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.153 X0.154 X0.155 X0.156 X0.157 X0.158 X0.159
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.160 X0.161 X0.162 X0.163 X0.164 X0.165 X0.166
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.167 X0.168 X0.169 X0.170 X0.171 X0.172 X0.173
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.174 X0.175 X0.176 X0.177 X0.178 X0.179 X0.180
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.181 X0.182 X0.183 X0.184 X0.185 X0.186 X0.187
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.188 X0.189 X0.190 X0.191 X0.192 X0.193 X0.194
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.195 X0.196 X0.197 X0.198 X0.199 X0.200 X0.201
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X84 X185 X159 X151 X60 X36 X0.202
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.203 X0.204 X0.205 X0.206 X0.207 X0.208 X0.209
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.210 X0.211 X0.212 X0.213 X0.214 X0.215 X0.216
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.217 X0.218 X0.219 X0.220 X0.221 X0.222 X0.223
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X222 X254 X254.1 X254.2 X254.3 X241 X198
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X198.1 X198.2 X198.3 X198.4 X198.5 X198.6 X198.7
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X170 X52 X0.224 X0.225 X0.226 X0.227 X0.228
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.229 X0.230 X0.231 X0.232 X0.233 X0.234 X0.235
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X67 X114 X72 X114.1 X163 X227 X254.4
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X225 X254.5 X254.6 X254.7 X250 X229 X254.8
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X254.9 X140 X0.236 X0.237 X0.238 X0.239 X0.240
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.241 X0.242 X0.243 X0.244 X0.245 X0.246 X0.247
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.248 X0.249 X0.250 X0.251 X0.252 X17 X66
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X14 X67.1 X67.2 X67.3 X59 X21 X236
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X254.10 X106 X0.253 X0.254 X0.255 X0.256 X0.257
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.258 X0.259 X0.260 X0.261 X0.262 X0.263 X0.264
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.265 X0.266 X0.267 X0.268 X0.269 X0.270 X0.271
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.272 X0.273 X0.274 X0.275 X0.276 X83 X253
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X209 X18 X0.277 X0.278 X0.279 X0.280 X0.281
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.282 X0.283 X0.284 X0.285 X0.286 X0.287 X0.288
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.289 X0.290 X0.291 X0.292 X0.293 X0.294 X0.295
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.296 X0.297 X0.298 X0.299 X22 X233 X255
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X83.1 X0.300 X0.301 X0.302 X0.303 X0.304 X0.305
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.306 X0.307 X0.308 X0.309 X0.310 X0.311 X0.312
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.313 X0.314 X0.315 X0.316 X0.317 X0.318 X0.319
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.320 X0.321 X0.322 X0.323 X129 X254.11 X238
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X44 X0.324 X0.325 X0.326 X0.327 X0.328 X0.329
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.330 X0.331 X0.332 X0.333 X0.334 X0.335 X0.336
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.337 X0.338 X0.339 X0.340 X0.341 X0.342 X0.343
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.344 X0.345 X0.346 X59.1 X249 X254.12 X62
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.347 X0.348 X0.349 X0.350 X0.351 X0.352 X0.353
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.354 X0.355 X0.356 X0.357 X0.358 X0.359 X0.360
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.361 X0.362 X0.363 X0.364 X0.365 X0.366 X0.367
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.368 X0.369 X0.370 X133 X254.13 X187 X5
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.371 X0.372 X0.373 X0.374 X0.375 X0.376 X0.377
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.378 X0.379 X0.380 X0.381 X0.382 X0.383 X0.384
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.385 X0.386 X0.387 X0.388 X0.389 X0.390 X0.391
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.392 X0.393 X9 X205 X248 X58 X0.394
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.395 X0.396 X0.397 X0.398 X0.399 X0.400 X0.401
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.402 X0.403 X0.404 X0.405 X0.406 X0.407 X0.408
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.409 X0.410 X0.411 X0.412 X0.413 X0.414 X0.415
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.416 X0.417 X126 X254.14 X182 X0.418 X0.419
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.420 X0.421 X0.422 X0.423 X0.424 X0.425 X0.426
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.427 X0.428 X0.429 X0.430 X0.431 X0.432 X0.433
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.434 X0.435 X0.436 X0.437 X0.438 X0.439 X0.440
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.441 X75 X251 X240 X57 X0.442 X0.443
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.444 X0.445 X0.446 X0.447 X0.448 X0.449 X0.450
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.451 X0.452 X0.453 X0.454 X0.455 X0.456 X0.457
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.458 X0.459 X0.460 X0.461 X0.462 X0.463 X0.464
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X19 X221 X254.15 X166 X0.465 X0.466 X0.467
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.468 X0.469 X0.470 X0.471 X0.472 X0.473 X0.474
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.475 X0.476 X0.477 X0.478 X0.479 X0.480 X0.481
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.482 X0.483 X0.484 X0.485 X0.486 X0.487 X3
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X203 X254.16 X219 X35 X0.488 X0.489 X0.490
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.491 X0.492 X0.493 X0.494 X0.495 X0.496 X0.497
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.498 X0.499 X0.500 X0.501 X0.502 X0.503 X0.504
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.505 X0.506 X0.507 X0.508 X0.509 X0.510 X38
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X254.17 X254.18 X77 X0.511 X0.512 X0.513 X0.514
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.515 X0.516 X0.517 X0.518 X0.519 X0.520 X0.521
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.522 X0.523 X0.524 X0.525 X0.526 X0.527 X0.528
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.529 X0.530 X0.531 X0.532 X0.533 X31 X224
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X254.19 X115 X1 X0.534 X0.535 X0.536 X0.537
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.538 X0.539 X0.540 X0.541 X0.542 X0.543 X0.544
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.545 X0.546 X0.547 X0.548 X0.549 X0.550 X0.551
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.552 X0.553 X0.554 X0.555 X0.556 X133.1 X254.20
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X254.21 X52.1 X0.557 X0.558 X0.559 X0.560 X0.561
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.562 X0.563 X0.564 X0.565 X0.566 X0.567 X0.568
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.569 X0.570 X0.571 X0.572 X0.573 X0.574 X0.575
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.576 X0.577 X0.578 X0.579 X61 X242 X254.22
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X254.23 X52.2 X0.580 X0.581 X0.582 X0.583 X0.584
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.585 X0.586 X0.587 X0.588 X0.589 X0.590 X0.591
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.592 X0.593 X0.594 X0.595 X0.596 X0.597 X0.598
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.599 X0.600 X0.601 X0.602 X121 X254.24 X254.25
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X219.1 X40 X0.603 X0.604 X0.605 X0.606 X0.607
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.608 X0.609 X0.610 X0.611 X0.612 X0.613 X0.614
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.615 X0.616 X0.617 X0.618 X0.619 X0.620 X0.621
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.622 X0.623 X0.624 X0.625 X121.1 X254.26 X207
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X18.1 X0.626 X0.627 X0.628 X0.629 X0.630 X0.631
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.632 X0.633 X0.634 X0.635 X0.636 X0.637 X0.638
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.639 X0.640 X0.641 X0.642 X0.643 X0.644 X0.645
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.646 X0.647 X0.648 X0.649 X0.650 X0.651 X0.652
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.653 X0.654 X0.655 X0.656 X0.657 X0.658 X0.659
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.660 X0.661 X0.662 X0.663 X0.664 X0.665 X0.666
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X0.667
## "integer"Pre-process Label as Factor in Training Set
Transformation: Transform Label as Factor (Categorical) and Change Column Names (TRAINING data set)
DATASET.train[, 1] <- as.factor(DATASET.train[, 1]) # As Category
colnames(DATASET.train) <- c("Y", paste("X.", 1:784, sep = "")) ## clearly we define the col from 2 to 785 as x1,x2,.....,x784.
class(DATASET.train[, 1]) ##changing to class format## [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" "integer" "integer" "integer" "integer" "integer" "integer"
## X.7 X.8 X.9 X.10 X.11 X.12 X.13
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.14 X.15 X.16 X.17 X.18 X.19 X.20
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.21 X.22 X.23 X.24 X.25 X.26 X.27
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.28 X.29 X.30 X.31 X.32 X.33 X.34
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.35 X.36 X.37 X.38 X.39 X.40 X.41
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.42 X.43 X.44 X.45 X.46 X.47 X.48
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.49 X.50 X.51 X.52 X.53 X.54 X.55
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.56 X.57 X.58 X.59 X.60 X.61 X.62
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.63 X.64 X.65 X.66 X.67 X.68 X.69
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.70 X.71 X.72 X.73 X.74 X.75 X.76
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.77 X.78 X.79 X.80 X.81 X.82 X.83
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.84 X.85 X.86 X.87 X.88 X.89 X.90
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.91 X.92 X.93 X.94 X.95 X.96 X.97
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.98 X.99 X.100 X.101 X.102 X.103 X.104
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.105 X.106 X.107 X.108 X.109 X.110 X.111
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.112 X.113 X.114 X.115 X.116 X.117 X.118
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.119 X.120 X.121 X.122 X.123 X.124 X.125
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.126 X.127 X.128 X.129 X.130 X.131 X.132
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.133 X.134 X.135 X.136 X.137 X.138 X.139
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.140 X.141 X.142 X.143 X.144 X.145 X.146
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.147 X.148 X.149 X.150 X.151 X.152 X.153
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.154 X.155 X.156 X.157 X.158 X.159 X.160
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.161 X.162 X.163 X.164 X.165 X.166 X.167
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.168 X.169 X.170 X.171 X.172 X.173 X.174
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.175 X.176 X.177 X.178 X.179 X.180 X.181
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.182 X.183 X.184 X.185 X.186 X.187 X.188
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.189 X.190 X.191 X.192 X.193 X.194 X.195
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.196 X.197 X.198 X.199 X.200 X.201 X.202
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.203 X.204 X.205 X.206 X.207 X.208 X.209
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.210 X.211 X.212 X.213 X.214 X.215 X.216
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.217 X.218 X.219 X.220 X.221 X.222 X.223
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.224 X.225 X.226 X.227 X.228 X.229 X.230
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.231 X.232 X.233 X.234 X.235 X.236 X.237
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.238 X.239 X.240 X.241 X.242 X.243 X.244
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.245 X.246 X.247 X.248 X.249 X.250 X.251
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.252 X.253 X.254 X.255 X.256 X.257 X.258
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.259 X.260 X.261 X.262 X.263 X.264 X.265
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.266 X.267 X.268 X.269 X.270 X.271 X.272
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.273 X.274 X.275 X.276 X.277 X.278 X.279
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.280 X.281 X.282 X.283 X.284 X.285 X.286
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.287 X.288 X.289 X.290 X.291 X.292 X.293
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.294 X.295 X.296 X.297 X.298 X.299 X.300
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.301 X.302 X.303 X.304 X.305 X.306 X.307
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.308 X.309 X.310 X.311 X.312 X.313 X.314
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.315 X.316 X.317 X.318 X.319 X.320 X.321
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.322 X.323 X.324 X.325 X.326 X.327 X.328
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.329 X.330 X.331 X.332 X.333 X.334 X.335
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.336 X.337 X.338 X.339 X.340 X.341 X.342
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.343 X.344 X.345 X.346 X.347 X.348 X.349
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.350 X.351 X.352 X.353 X.354 X.355 X.356
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.357 X.358 X.359 X.360 X.361 X.362 X.363
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.364 X.365 X.366 X.367 X.368 X.369 X.370
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.371 X.372 X.373 X.374 X.375 X.376 X.377
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.378 X.379 X.380 X.381 X.382 X.383 X.384
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.385 X.386 X.387 X.388 X.389 X.390 X.391
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.392 X.393 X.394 X.395 X.396 X.397 X.398
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.399 X.400 X.401 X.402 X.403 X.404 X.405
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.406 X.407 X.408 X.409 X.410 X.411 X.412
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.413 X.414 X.415 X.416 X.417 X.418 X.419
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.420 X.421 X.422 X.423 X.424 X.425 X.426
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.427 X.428 X.429 X.430 X.431 X.432 X.433
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.434 X.435 X.436 X.437 X.438 X.439 X.440
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.441 X.442 X.443 X.444 X.445 X.446 X.447
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.448 X.449 X.450 X.451 X.452 X.453 X.454
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.455 X.456 X.457 X.458 X.459 X.460 X.461
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.462 X.463 X.464 X.465 X.466 X.467 X.468
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.469 X.470 X.471 X.472 X.473 X.474 X.475
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.476 X.477 X.478 X.479 X.480 X.481 X.482
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.483 X.484 X.485 X.486 X.487 X.488 X.489
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.490 X.491 X.492 X.493 X.494 X.495 X.496
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.497 X.498 X.499 X.500 X.501 X.502 X.503
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.504 X.505 X.506 X.507 X.508 X.509 X.510
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.511 X.512 X.513 X.514 X.515 X.516 X.517
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.518 X.519 X.520 X.521 X.522 X.523 X.524
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.525 X.526 X.527 X.528 X.529 X.530 X.531
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.532 X.533 X.534 X.535 X.536 X.537 X.538
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.539 X.540 X.541 X.542 X.543 X.544 X.545
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.546 X.547 X.548 X.549 X.550 X.551 X.552
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.553 X.554 X.555 X.556 X.557 X.558 X.559
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.560 X.561 X.562 X.563 X.564 X.565 X.566
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.567 X.568 X.569 X.570 X.571 X.572 X.573
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.574 X.575 X.576 X.577 X.578 X.579 X.580
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.581 X.582 X.583 X.584 X.585 X.586 X.587
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.588 X.589 X.590 X.591 X.592 X.593 X.594
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.595 X.596 X.597 X.598 X.599 X.600 X.601
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.602 X.603 X.604 X.605 X.606 X.607 X.608
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.609 X.610 X.611 X.612 X.613 X.614 X.615
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.616 X.617 X.618 X.619 X.620 X.621 X.622
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.623 X.624 X.625 X.626 X.627 X.628 X.629
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.630 X.631 X.632 X.633 X.634 X.635 X.636
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.637 X.638 X.639 X.640 X.641 X.642 X.643
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.644 X.645 X.646 X.647 X.648 X.649 X.650
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.651 X.652 X.653 X.654 X.655 X.656 X.657
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.658 X.659 X.660 X.661 X.662 X.663 X.664
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.665 X.666 X.667 X.668 X.669 X.670 X.671
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.672 X.673 X.674 X.675 X.676 X.677 X.678
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.679 X.680 X.681 X.682 X.683 X.684 X.685
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.686 X.687 X.688 X.689 X.690 X.691 X.692
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.693 X.694 X.695 X.696 X.697 X.698 X.699
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.700 X.701 X.702 X.703 X.704 X.705 X.706
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.707 X.708 X.709 X.710 X.711 X.712 X.713
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.714 X.715 X.716 X.717 X.718 X.719 X.720
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.721 X.722 X.723 X.724 X.725 X.726 X.727
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.728 X.729 X.730 X.731 X.732 X.733 X.734
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.735 X.736 X.737 X.738 X.739 X.740 X.741
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.742 X.743 X.744 X.745 X.746 X.747 X.748
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.749 X.750 X.751 X.752 X.753 X.754 X.755
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.756 X.757 X.758 X.759 X.760 X.761 X.762
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.763 X.764 X.765 X.766 X.767 X.768 X.769
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.770 X.771 X.772 X.773 X.774 X.775 X.776
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.777 X.778 X.779 X.780 X.781 X.782 X.783
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.784
## "integer"Pre-process Label as Factor in Testing Set
Transformation: Transform Label as Factor (Categorical) and Change Column Names (TESTING data set)
DATASET.test[, 1] <- as.factor(DATASET.test[, 1]) # As Category
colnames(DATASET.test) <- c("Y", paste("X.", 1:784, sep = "")) ##similar to the training set
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" "integer" "integer" "integer" "integer" "integer" "integer"
## X.7 X.8 X.9 X.10 X.11 X.12 X.13
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.14 X.15 X.16 X.17 X.18 X.19 X.20
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.21 X.22 X.23 X.24 X.25 X.26 X.27
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.28 X.29 X.30 X.31 X.32 X.33 X.34
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.35 X.36 X.37 X.38 X.39 X.40 X.41
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.42 X.43 X.44 X.45 X.46 X.47 X.48
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.49 X.50 X.51 X.52 X.53 X.54 X.55
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.56 X.57 X.58 X.59 X.60 X.61 X.62
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.63 X.64 X.65 X.66 X.67 X.68 X.69
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.70 X.71 X.72 X.73 X.74 X.75 X.76
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.77 X.78 X.79 X.80 X.81 X.82 X.83
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.84 X.85 X.86 X.87 X.88 X.89 X.90
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.91 X.92 X.93 X.94 X.95 X.96 X.97
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.98 X.99 X.100 X.101 X.102 X.103 X.104
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.105 X.106 X.107 X.108 X.109 X.110 X.111
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.112 X.113 X.114 X.115 X.116 X.117 X.118
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.119 X.120 X.121 X.122 X.123 X.124 X.125
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.126 X.127 X.128 X.129 X.130 X.131 X.132
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.133 X.134 X.135 X.136 X.137 X.138 X.139
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.140 X.141 X.142 X.143 X.144 X.145 X.146
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.147 X.148 X.149 X.150 X.151 X.152 X.153
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.154 X.155 X.156 X.157 X.158 X.159 X.160
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.161 X.162 X.163 X.164 X.165 X.166 X.167
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.168 X.169 X.170 X.171 X.172 X.173 X.174
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.175 X.176 X.177 X.178 X.179 X.180 X.181
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.182 X.183 X.184 X.185 X.186 X.187 X.188
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.189 X.190 X.191 X.192 X.193 X.194 X.195
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.196 X.197 X.198 X.199 X.200 X.201 X.202
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.203 X.204 X.205 X.206 X.207 X.208 X.209
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.210 X.211 X.212 X.213 X.214 X.215 X.216
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.217 X.218 X.219 X.220 X.221 X.222 X.223
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.224 X.225 X.226 X.227 X.228 X.229 X.230
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.231 X.232 X.233 X.234 X.235 X.236 X.237
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.238 X.239 X.240 X.241 X.242 X.243 X.244
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.245 X.246 X.247 X.248 X.249 X.250 X.251
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.252 X.253 X.254 X.255 X.256 X.257 X.258
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.259 X.260 X.261 X.262 X.263 X.264 X.265
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.266 X.267 X.268 X.269 X.270 X.271 X.272
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.273 X.274 X.275 X.276 X.277 X.278 X.279
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.280 X.281 X.282 X.283 X.284 X.285 X.286
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.287 X.288 X.289 X.290 X.291 X.292 X.293
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.294 X.295 X.296 X.297 X.298 X.299 X.300
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.301 X.302 X.303 X.304 X.305 X.306 X.307
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.308 X.309 X.310 X.311 X.312 X.313 X.314
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.315 X.316 X.317 X.318 X.319 X.320 X.321
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.322 X.323 X.324 X.325 X.326 X.327 X.328
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.329 X.330 X.331 X.332 X.333 X.334 X.335
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.336 X.337 X.338 X.339 X.340 X.341 X.342
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.343 X.344 X.345 X.346 X.347 X.348 X.349
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.350 X.351 X.352 X.353 X.354 X.355 X.356
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.357 X.358 X.359 X.360 X.361 X.362 X.363
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.364 X.365 X.366 X.367 X.368 X.369 X.370
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.371 X.372 X.373 X.374 X.375 X.376 X.377
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.378 X.379 X.380 X.381 X.382 X.383 X.384
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.385 X.386 X.387 X.388 X.389 X.390 X.391
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.392 X.393 X.394 X.395 X.396 X.397 X.398
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.399 X.400 X.401 X.402 X.403 X.404 X.405
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.406 X.407 X.408 X.409 X.410 X.411 X.412
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.413 X.414 X.415 X.416 X.417 X.418 X.419
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.420 X.421 X.422 X.423 X.424 X.425 X.426
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.427 X.428 X.429 X.430 X.431 X.432 X.433
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.434 X.435 X.436 X.437 X.438 X.439 X.440
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.441 X.442 X.443 X.444 X.445 X.446 X.447
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.448 X.449 X.450 X.451 X.452 X.453 X.454
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.455 X.456 X.457 X.458 X.459 X.460 X.461
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.462 X.463 X.464 X.465 X.466 X.467 X.468
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.469 X.470 X.471 X.472 X.473 X.474 X.475
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.476 X.477 X.478 X.479 X.480 X.481 X.482
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.483 X.484 X.485 X.486 X.487 X.488 X.489
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.490 X.491 X.492 X.493 X.494 X.495 X.496
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.497 X.498 X.499 X.500 X.501 X.502 X.503
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.504 X.505 X.506 X.507 X.508 X.509 X.510
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.511 X.512 X.513 X.514 X.515 X.516 X.517
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.518 X.519 X.520 X.521 X.522 X.523 X.524
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.525 X.526 X.527 X.528 X.529 X.530 X.531
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.532 X.533 X.534 X.535 X.536 X.537 X.538
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.539 X.540 X.541 X.542 X.543 X.544 X.545
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.546 X.547 X.548 X.549 X.550 X.551 X.552
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.553 X.554 X.555 X.556 X.557 X.558 X.559
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.560 X.561 X.562 X.563 X.564 X.565 X.566
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.567 X.568 X.569 X.570 X.571 X.572 X.573
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.574 X.575 X.576 X.577 X.578 X.579 X.580
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.581 X.582 X.583 X.584 X.585 X.586 X.587
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.588 X.589 X.590 X.591 X.592 X.593 X.594
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.595 X.596 X.597 X.598 X.599 X.600 X.601
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.602 X.603 X.604 X.605 X.606 X.607 X.608
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.609 X.610 X.611 X.612 X.613 X.614 X.615
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.616 X.617 X.618 X.619 X.620 X.621 X.622
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.623 X.624 X.625 X.626 X.627 X.628 X.629
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.630 X.631 X.632 X.633 X.634 X.635 X.636
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.637 X.638 X.639 X.640 X.641 X.642 X.643
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.644 X.645 X.646 X.647 X.648 X.649 X.650
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.651 X.652 X.653 X.654 X.655 X.656 X.657
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.658 X.659 X.660 X.661 X.662 X.663 X.664
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.665 X.666 X.667 X.668 X.669 X.670 X.671
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.672 X.673 X.674 X.675 X.676 X.677 X.678
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.679 X.680 X.681 X.682 X.683 X.684 X.685
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.686 X.687 X.688 X.689 X.690 X.691 X.692
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.693 X.694 X.695 X.696 X.697 X.698 X.699
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.700 X.701 X.702 X.703 X.704 X.705 X.706
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.707 X.708 X.709 X.710 X.711 X.712 X.713
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.714 X.715 X.716 X.717 X.718 X.719 X.720
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.721 X.722 X.723 X.724 X.725 X.726 X.727
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.728 X.729 X.730 X.731 X.732 X.733 X.734
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.735 X.736 X.737 X.738 X.739 X.740 X.741
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.742 X.743 X.744 X.745 X.746 X.747 X.748
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.749 X.750 X.751 X.752 X.753 X.754 X.755
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.756 X.757 X.758 X.759 X.760 X.761 X.762
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.763 X.764 X.765 X.766 X.767 X.768 X.769
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.770 X.771 X.772 X.773 X.774 X.775 X.776
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.777 X.778 X.779 X.780 X.781 X.782 X.783
## "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## X.784
## "integer"Checking Training Set
Total Number of Digits (Training Set) bar plot and pie chart. it shows how many are the 0,1,2,3,4,5,6,7,8,9, in the dataset. what are their distribution and count. their % of spread through out the dataset.
##taking only the response or target varible
resTable <- table(DATASET.train$Y)
##bar plot
par(mfrow = c(1, 1))
# increase y-axis margin.
par(mar = c(5, 4, 4, 2) + 0.1)
plot <- plot(DATASET.train$Y, main = "Total Number of Digits (Training Set)",
ylim = c(0, 9000), ylab = "Examples Number")
text(x = plot, y = resTable + 50, labels = resTable, cex = 0.75)
##pie chart
par(mfrow = c(1, 1))
percentage <- round(resTable/sum(resTable) * 100)
# add percents to labels
labels <- paste0(row.names(resTable), " (", percentage, "%) ")
pie(resTable, labels = labels, main = "Total Number of Digits (Training Set)")
Checking Testing Set
Total Number of Digits (Testing Set) bar plot and piechart (similar to train 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, main = "Total Number of Digits (Testing Set)",
ylim = c(0, 3000), 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, main = "Total Number of Digits (Testing Set)") 
STEP 2: Build Predictive Model
Build the Predictive Model on Training DataSet
Naive Bayes algorithm:
Our job is to look at the evidence, to consider how likely it is to be this class or that class, and assign a label to each entity. Again, we take a very simple approach: The class that has the highest probability is declared the “winner” and that class label gets assigned to that combination of evidences.
Naive Bayes comes under supervise machine learning which used to make classifications of data sets. It is used to predict things based on it’s prior knowledge and independence assumptions.
They call it naive because it’s assumptions (it assumes that all of the features in the data set are equally important and independent) are really optimistic and rarely true in most real-world applications.
It is classification algorithm which makes decision for unknown data set. It is based on Bayes Theorem which describe the probability of event based on it’s prior knowledge.
pc <- proc.time() ## mention the time taken to run the classification model.
model.naiveBayes <- naiveBayes(DATASET.train$Y ~ ., data = DATASET.train) ##training the model with naive bayes algo under supervised modelling technique
proc.time() - pc## user system elapsed
## 2 0 2summary(model.naiveBayes) ##output of the model## Length Class Mode
## apriori 10 table numeric
## tables 784 -none- list
## levels 10 -none- character
## call 4 -none- callVerify the Predictive Model on Testing DataSet
Confusion Matrix (naiveBayes) it gives you how are the variances are spread over the model and what are the sensitivity and accuracy
prediction.naiveBayes <- predict(model.naiveBayes, newdata = DATASET.test, type = "class") ##prediction function predicts the test target value from the model generated from train set.
table(`Actual Class` = DATASET.test$Y, `Predicted Class` = prediction.naiveBayes)## Predicted Class
## Actual Class 0 1 2 3 4 5 6 7 8 9
## 0 862 0 1 3 3 4 31 1 44 31
## 1 0 1081 1 0 0 0 10 0 36 7
## 2 94 24 212 87 2 2 284 5 298 24
## 3 40 33 4 310 1 4 59 7 437 115
## 4 22 2 2 0 131 4 66 7 188 560
## 5 69 25 1 19 2 35 39 1 592 109
## 6 16 13 2 0 1 4 890 0 27 5
## 7 2 14 0 7 8 2 5 228 49 712
## 8 13 67 4 6 2 7 13 3 642 217
## 9 8 7 1 5 0 0 2 3 22 961error.rate.naiveBayes <- sum(DATASET.test$Y != prediction.naiveBayes)/nrow(DATASET.test) ##error classification and accuracy checking algorithm
print(paste0("Accuary (Precision): ", 1 - error.rate.naiveBayes))##printing the accuracy output## [1] "Accuary (Precision): 0.535253525352535"STEP 3: Apply the Predictive Model
Apply the Model to sample images and comared with label
Predict Digit for row 1 of test set. lets check how are model is working. we take the 1st row and check whether our prediction is correct or not.
# All Prediction for Row 1
row <- 1
prediction.digit <- as.vector(predict(model.naiveBayes, newdata = DATASET.test[row, ], type = "class"))
print(paste0("Current Digit: ", as.character(DATASET.test$Y[row])))## [1] "Current Digit: 2"print(paste0("Predicted Digit: ", prediction.digit))## [1] "Predicted Digit: 2"