CaretHeart
Sofia Berron
2024-08-21
library(ggplot2)
library(lattice)
library(caret)
library(datasets)
library(DataExplorer)
library(kernlab)##
## Attaching package: 'kernlab'## The following object is masked from 'package:ggplot2':
##
## alphalibrary(randomForest)## randomForest 4.7-1.1## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:ggplot2':
##
## margindf <- read.csv("~/Downloads/heart.csv")
summary(df)## age sex cp trestbps
## Min. :29.00 Min. :0.0000 Min. :0.0000 Min. : 94.0
## 1st Qu.:48.00 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:120.0
## Median :56.00 Median :1.0000 Median :1.0000 Median :130.0
## Mean :54.43 Mean :0.6956 Mean :0.9424 Mean :131.6
## 3rd Qu.:61.00 3rd Qu.:1.0000 3rd Qu.:2.0000 3rd Qu.:140.0
## Max. :77.00 Max. :1.0000 Max. :3.0000 Max. :200.0
## chol fbs restecg thalach
## Min. :126 Min. :0.0000 Min. :0.0000 Min. : 71.0
## 1st Qu.:211 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:132.0
## Median :240 Median :0.0000 Median :1.0000 Median :152.0
## Mean :246 Mean :0.1493 Mean :0.5298 Mean :149.1
## 3rd Qu.:275 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:166.0
## Max. :564 Max. :1.0000 Max. :2.0000 Max. :202.0
## exang oldpeak slope ca
## Min. :0.0000 Min. :0.000 Min. :0.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:1.000 1st Qu.:0.0000
## Median :0.0000 Median :0.800 Median :1.000 Median :0.0000
## Mean :0.3366 Mean :1.072 Mean :1.385 Mean :0.7541
## 3rd Qu.:1.0000 3rd Qu.:1.800 3rd Qu.:2.000 3rd Qu.:1.0000
## Max. :1.0000 Max. :6.200 Max. :2.000 Max. :4.0000
## thal target
## Min. :0.000 Min. :0.0000
## 1st Qu.:2.000 1st Qu.:0.0000
## Median :2.000 Median :1.0000
## Mean :2.324 Mean :0.5132
## 3rd Qu.:3.000 3rd Qu.:1.0000
## Max. :3.000 Max. :1.0000str(df)## 'data.frame': 1025 obs. of 14 variables:
## $ age : int 52 53 70 61 62 58 58 55 46 54 ...
## $ sex : int 1 1 1 1 0 0 1 1 1 1 ...
## $ cp : int 0 0 0 0 0 0 0 0 0 0 ...
## $ trestbps: int 125 140 145 148 138 100 114 160 120 122 ...
## $ chol : int 212 203 174 203 294 248 318 289 249 286 ...
## $ fbs : int 0 1 0 0 1 0 0 0 0 0 ...
## $ restecg : int 1 0 1 1 1 0 2 0 0 0 ...
## $ thalach : int 168 155 125 161 106 122 140 145 144 116 ...
## $ exang : int 0 1 1 0 0 0 0 1 0 1 ...
## $ oldpeak : num 1 3.1 2.6 0 1.9 1 4.4 0.8 0.8 3.2 ...
## $ slope : int 2 0 0 2 1 1 0 1 2 1 ...
## $ ca : int 2 0 0 1 3 0 3 1 0 2 ...
## $ thal : int 3 3 3 3 2 2 1 3 3 2 ...
## $ target : int 0 0 0 0 0 1 0 0 0 0 ...plot_missing(df)
set.seed(123)
renglones_entrenamiento <- createDataPartition(df$target, p=0.8, list=FALSE)
entrenamiento <- df[renglones_entrenamiento, ]
prueba <- df[-renglones_entrenamiento, ]
prueba## age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal
## 1 52 1 0 125 212 0 1 168 0 1.0 2 2 3
## 3 70 1 0 145 174 0 1 125 1 2.6 0 0 3
## 7 58 1 0 114 318 0 2 140 0 4.4 0 3 1
## 9 46 1 0 120 249 0 0 144 0 0.8 2 0 3
## 12 43 0 0 132 341 1 0 136 1 3.0 1 0 3
## 15 52 1 0 128 204 1 1 156 1 1.0 1 0 0
## 22 67 0 0 106 223 0 1 142 0 0.3 2 2 2
## 25 42 0 2 120 209 0 1 173 0 0.0 1 0 2
## 27 44 1 2 130 233 0 1 179 1 0.4 2 0 2
## 28 58 0 1 136 319 1 0 152 0 0.0 2 2 2
## 32 50 0 1 120 244 0 1 162 0 1.1 2 0 2
## 35 50 1 2 129 196 0 1 163 0 0.0 2 0 2
## 43 61 0 0 130 330 0 0 169 0 0.0 2 0 2
## 47 41 1 1 135 203 0 1 132 0 0.0 1 0 1
## 60 57 1 1 154 232 0 0 164 0 0.0 2 1 2
## 63 67 1 0 100 299 0 0 125 1 0.9 1 2 2
## 66 59 1 3 170 288 0 0 159 0 0.2 1 0 3
## 70 62 0 0 160 164 0 0 145 0 6.2 0 3 3
## 73 56 1 0 125 249 1 0 144 1 1.2 1 1 2
## 75 48 1 0 130 256 1 0 150 1 0.0 2 2 3
## 86 44 1 1 120 220 0 1 170 0 0.0 2 0 2
## 97 57 0 0 128 303 0 0 159 0 0.0 2 1 2
## 101 43 0 2 122 213 0 1 165 0 0.2 1 0 2
## 102 57 1 2 150 126 1 1 173 0 0.2 2 1 3
## 109 40 1 0 152 223 0 1 181 0 0.0 2 0 3
## 126 60 0 3 150 240 0 1 171 0 0.9 2 0 2
## 132 51 0 2 130 256 0 0 149 0 0.5 2 0 2
## 133 41 1 1 135 203 0 1 132 0 0.0 1 0 1
## 140 41 1 1 110 235 0 1 153 0 0.0 2 0 2
## 142 63 0 0 124 197 0 1 136 1 0.0 1 0 2
## 144 34 1 3 118 182 0 0 174 0 0.0 2 0 2
## 145 47 1 0 112 204 0 1 143 0 0.1 2 0 2
## 147 51 0 2 120 295 0 0 157 0 0.6 2 0 2
## 149 52 1 3 152 298 1 1 178 0 1.2 1 0 3
## 150 39 1 2 140 321 0 0 182 0 0.0 2 0 2
## 154 54 1 2 120 258 0 0 147 0 0.4 1 0 3
## 156 54 1 1 108 309 0 1 156 0 0.0 2 0 3
## 157 40 1 3 140 199 0 1 178 1 1.4 2 0 3
## 174 39 0 2 94 199 0 1 179 0 0.0 2 0 2
## 176 56 0 0 200 288 1 0 133 1 4.0 0 2 3
## 182 64 1 3 110 211 0 0 144 1 1.8 1 0 2
## 183 60 1 0 140 293 0 0 170 0 1.2 1 2 3
## 192 56 1 1 130 221 0 0 163 0 0.0 2 0 3
## 198 45 1 0 115 260 0 0 185 0 0.0 2 0 2
## 202 34 1 3 118 182 0 0 174 0 0.0 2 0 2
## 208 41 1 2 112 250 0 1 179 0 0.0 2 0 2
## 215 45 1 1 128 308 0 0 170 0 0.0 2 0 2
## 216 49 1 1 130 266 0 1 171 0 0.6 2 0 2
## 233 60 1 0 125 258 0 0 141 1 2.8 1 1 3
## 245 51 1 2 125 245 1 0 166 0 2.4 1 0 2
## 249 39 0 2 138 220 0 1 152 0 0.0 1 0 2
## 253 55 1 0 132 353 0 1 132 1 1.2 1 1 3
## 254 57 1 0 165 289 1 0 124 0 1.0 1 3 3
## 257 35 0 0 138 183 0 1 182 0 1.4 2 0 2
## 269 58 1 2 132 224 0 0 173 0 3.2 2 2 3
## 272 44 1 1 120 263 0 1 173 0 0.0 2 0 3
## 283 41 0 1 130 204 0 0 172 0 1.4 2 0 2
## 285 58 1 2 132 224 0 0 173 0 3.2 2 2 3
## 288 71 0 1 160 302 0 1 162 0 0.4 2 2 2
## 293 61 1 2 150 243 1 1 137 1 1.0 1 0 2
## 296 67 1 0 100 299 0 0 125 1 0.9 1 2 2
## 300 52 1 1 120 325 0 1 172 0 0.2 2 0 2
## 305 52 0 2 136 196 0 0 169 0 0.1 1 0 2
## 307 44 0 2 118 242 0 1 149 0 0.3 1 1 2
## 312 48 1 0 130 256 1 0 150 1 0.0 2 2 3
## 313 70 1 2 160 269 0 1 112 1 2.9 1 1 3
## 314 74 0 1 120 269 0 0 121 1 0.2 2 1 2
## 318 63 0 2 135 252 0 0 172 0 0.0 2 0 2
## 321 53 0 0 130 264 0 0 143 0 0.4 1 0 2
## 333 37 1 2 130 250 0 1 187 0 3.5 0 0 2
## 345 41 1 1 120 157 0 1 182 0 0.0 2 0 2
## 351 66 1 0 120 302 0 0 151 0 0.4 1 0 2
## 353 57 1 0 110 335 0 1 143 1 3.0 1 1 3
## 356 46 0 0 138 243 0 0 152 1 0.0 1 0 2
## 360 53 0 2 128 216 0 0 115 0 0.0 2 0 0
## 361 48 1 0 122 222 0 0 186 0 0.0 2 0 2
## 363 43 0 2 122 213 0 1 165 0 0.2 1 0 2
## 367 58 1 2 112 230 0 0 165 0 2.5 1 1 3
## 369 58 1 2 105 240 0 0 154 1 0.6 1 0 3
## 372 55 1 0 132 353 0 1 132 1 1.2 1 1 3
## 375 46 0 2 142 177 0 0 160 1 1.4 0 0 2
## 376 66 1 0 160 228 0 0 138 0 2.3 2 0 1
## 383 59 1 0 110 239 0 0 142 1 1.2 1 1 3
## 385 35 1 0 126 282 0 0 156 1 0.0 2 0 3
## 408 58 1 0 100 234 0 1 156 0 0.1 2 1 3
## 410 46 1 2 150 231 0 1 147 0 3.6 1 0 2
## 411 41 0 1 105 198 0 1 168 0 0.0 2 1 2
## 423 57 0 0 120 354 0 1 163 1 0.6 2 0 2
## 425 45 1 0 142 309 0 0 147 1 0.0 1 3 3
## 427 54 0 2 160 201 0 1 163 0 0.0 2 1 2
## 432 65 0 0 150 225 0 0 114 0 1.0 1 3 3
## 434 37 1 2 130 250 0 1 187 0 3.5 0 0 2
## 436 57 0 0 120 354 0 1 163 1 0.6 2 0 2
## 439 47 1 2 130 253 0 1 179 0 0.0 2 0 2
## 444 57 1 2 150 168 0 1 174 0 1.6 2 0 2
## 453 66 0 0 178 228 1 1 165 1 1.0 1 2 3
## 454 49 0 1 134 271 0 1 162 0 0.0 1 0 2
## 460 51 1 0 140 261 0 0 186 1 0.0 2 0 2
## 470 67 1 0 160 286 0 0 108 1 1.5 1 3 2
## 471 60 0 3 150 240 0 1 171 0 0.9 2 0 2
## 484 35 1 1 122 192 0 1 174 0 0.0 2 0 2
## 487 41 1 0 110 172 0 0 158 0 0.0 2 0 3
## 488 65 1 0 135 254 0 0 127 0 2.8 1 1 3
## 495 51 1 2 125 245 1 0 166 0 2.4 1 0 2
## 496 59 1 0 135 234 0 1 161 0 0.5 1 0 3
## 497 68 1 2 180 274 1 0 150 1 1.6 1 0 3
## 501 71 0 0 112 149 0 1 125 0 1.6 1 0 2
## 506 44 0 2 118 242 0 1 149 0 0.3 1 1 2
## 507 61 1 0 120 260 0 1 140 1 3.6 1 1 3
## 513 44 1 0 112 290 0 0 153 0 0.0 2 1 2
## 515 44 1 1 120 220 0 1 170 0 0.0 2 0 2
## 520 61 1 0 148 203 0 1 161 0 0.0 2 1 3
## 521 59 1 0 140 177 0 1 162 1 0.0 2 1 3
## 525 58 1 2 112 230 0 0 165 0 2.5 1 1 3
## 530 69 1 3 160 234 1 0 131 0 0.1 1 1 2
## 532 65 0 2 155 269 0 1 148 0 0.8 2 0 2
## 535 54 0 2 108 267 0 0 167 0 0.0 2 0 2
## 536 76 0 2 140 197 0 2 116 0 1.1 1 0 2
## 546 48 1 1 110 229 0 1 168 0 1.0 0 0 3
## 550 68 1 2 118 277 0 1 151 0 1.0 2 1 3
## 552 54 1 0 122 286 0 0 116 1 3.2 1 2 2
## 556 67 1 0 125 254 1 1 163 0 0.2 1 2 3
## 558 48 1 0 122 222 0 0 186 0 0.0 2 0 2
## 561 58 0 0 130 197 0 1 131 0 0.6 1 0 2
## 565 56 1 0 132 184 0 0 105 1 2.1 1 1 1
## 571 54 0 2 135 304 1 1 170 0 0.0 2 0 2
## 572 60 1 0 145 282 0 0 142 1 2.8 1 2 3
## 576 43 1 0 150 247 0 1 171 0 1.5 2 0 2
## 583 55 1 1 130 262 0 1 155 0 0.0 2 0 2
## 584 43 1 0 120 177 0 0 120 1 2.5 1 0 3
## 587 64 1 2 125 309 0 1 131 1 1.8 1 0 3
## 591 74 0 1 120 269 0 0 121 1 0.2 2 1 2
## 592 63 0 0 108 269 0 1 169 1 1.8 1 2 2
## 596 61 1 0 148 203 0 1 161 0 0.0 2 1 3
## 600 63 0 1 140 195 0 1 179 0 0.0 2 2 2
## 607 66 1 0 112 212 0 0 132 1 0.1 2 1 2
## 612 55 0 0 128 205 0 2 130 1 2.0 1 1 3
## 620 65 1 0 110 248 0 0 158 0 0.6 2 2 1
## 624 61 1 3 134 234 0 1 145 0 2.6 1 2 2
## 629 69 0 3 140 239 0 1 151 0 1.8 2 2 2
## 635 52 1 0 125 212 0 1 168 0 1.0 2 2 3
## 640 58 0 0 130 197 0 1 131 0 0.6 1 0 2
## 641 46 0 0 138 243 0 0 152 1 0.0 1 0 2
## 659 64 1 2 125 309 0 1 131 1 1.8 1 0 3
## 662 58 1 0 114 318 0 2 140 0 4.4 0 3 1
## 667 35 1 1 122 192 0 1 174 0 0.0 2 0 2
## 681 42 1 1 120 295 0 1 162 0 0.0 2 0 2
## 687 52 1 0 128 204 1 1 156 1 1.0 1 0 0
## 689 56 0 0 200 288 1 0 133 1 4.0 0 2 3
## 702 35 1 0 120 198 0 1 130 1 1.6 1 0 3
## 713 45 0 1 112 160 0 1 138 0 0.0 1 0 2
## 716 70 1 1 156 245 0 0 143 0 0.0 2 0 2
## 717 55 0 0 128 205 0 2 130 1 2.0 1 1 3
## 723 67 0 2 152 277 0 1 172 0 0.0 2 1 2
## 725 74 0 1 120 269 0 0 121 1 0.2 2 1 2
## 728 56 1 1 130 221 0 0 163 0 0.0 2 0 3
## 730 55 0 1 135 250 0 0 161 0 1.4 1 0 2
## 734 44 0 2 108 141 0 1 175 0 0.6 1 0 2
## 740 52 1 0 128 255 0 1 161 1 0.0 2 1 3
## 742 41 0 2 112 268 0 0 172 1 0.0 2 0 2
## 743 63 1 0 130 330 1 0 132 1 1.8 2 3 3
## 748 60 1 0 117 230 1 1 160 1 1.4 2 2 3
## 750 58 1 1 125 220 0 1 144 0 0.4 1 4 3
## 760 61 0 0 130 330 0 0 169 0 0.0 2 0 2
## 777 61 0 0 145 307 0 0 146 1 1.0 1 0 3
## 782 58 1 0 146 218 0 1 105 0 2.0 1 1 3
## 790 62 1 1 120 281 0 0 103 0 1.4 1 1 3
## 795 61 1 3 134 234 0 1 145 0 2.6 1 2 2
## 801 67 1 0 120 229 0 0 129 1 2.6 1 2 3
## 805 58 0 0 130 197 0 1 131 0 0.6 1 0 2
## 810 54 0 2 110 214 0 1 158 0 1.6 1 0 2
## 812 57 1 1 124 261 0 1 141 0 0.3 2 0 3
## 825 61 1 0 138 166 0 0 125 1 3.6 1 1 2
## 845 60 1 0 140 293 0 0 170 0 1.2 1 2 3
## 846 56 1 0 132 184 0 0 105 1 2.1 1 1 1
## 848 61 1 0 138 166 0 0 125 1 3.6 1 1 2
## 849 58 0 3 150 283 1 0 162 0 1.0 2 0 2
## 852 37 1 2 130 250 0 1 187 0 3.5 0 0 2
## 863 59 1 3 170 288 0 0 159 0 0.2 1 0 3
## 868 41 1 1 110 235 0 1 153 0 0.0 2 0 2
## 884 48 1 0 124 274 0 0 166 0 0.5 1 0 3
## 886 57 1 0 165 289 1 0 124 0 1.0 1 3 3
## 889 60 0 0 150 258 0 0 157 0 2.6 1 2 3
## 894 52 1 0 128 204 1 1 156 1 1.0 1 0 0
## 900 59 1 0 135 234 0 1 161 0 0.5 1 0 3
## 901 61 1 3 134 234 0 1 145 0 2.6 1 2 2
## 904 59 1 2 126 218 1 1 134 0 2.2 1 1 1
## 918 47 1 2 130 253 0 1 179 0 0.0 2 0 2
## 930 60 1 0 130 206 0 0 132 1 2.4 1 2 3
## 933 51 0 2 140 308 0 0 142 0 1.5 2 1 2
## 941 57 0 0 140 241 0 1 123 1 0.2 1 0 3
## 948 54 0 2 160 201 0 1 163 0 0.0 2 1 2
## 952 62 0 2 130 263 0 1 97 0 1.2 1 1 3
## 961 52 0 2 136 196 0 0 169 0 0.1 1 0 2
## 969 53 1 0 140 203 1 0 155 1 3.1 0 0 3
## 974 51 1 2 125 245 1 0 166 0 2.4 1 0 2
## 984 64 1 0 128 263 0 1 105 1 0.2 1 1 3
## 991 56 1 1 120 236 0 1 178 0 0.8 2 0 2
## 995 59 1 0 110 239 0 0 142 1 1.2 1 1 3
## 996 44 1 1 120 263 0 1 173 0 0.0 2 0 3
## 1002 42 1 0 140 226 0 1 178 0 0.0 2 0 2
## 1011 51 1 0 140 299 0 1 173 1 1.6 2 0 3
## 1014 58 1 0 114 318 0 2 140 0 4.4 0 3 1
## 1018 53 1 0 123 282 0 1 95 1 2.0 1 2 3
## 1022 60 1 0 125 258 0 0 141 1 2.8 1 1 3
## target
## 1 0
## 3 0
## 7 0
## 9 0
## 12 0
## 15 0
## 22 1
## 25 1
## 27 1
## 28 0
## 32 1
## 35 1
## 43 0
## 47 1
## 60 0
## 63 0
## 66 0
## 70 0
## 73 0
## 75 0
## 86 1
## 97 1
## 101 1
## 102 1
## 109 0
## 126 1
## 132 1
## 133 1
## 140 1
## 142 0
## 144 1
## 145 1
## 147 1
## 149 1
## 150 1
## 154 1
## 156 1
## 157 1
## 174 1
## 176 0
## 182 1
## 183 0
## 192 1
## 198 1
## 202 1
## 208 1
## 215 1
## 216 1
## 233 0
## 245 1
## 249 1
## 253 0
## 254 0
## 257 1
## 269 0
## 272 1
## 283 1
## 285 0
## 288 1
## 293 1
## 296 0
## 300 1
## 305 1
## 307 1
## 312 0
## 313 0
## 314 1
## 318 1
## 321 1
## 333 1
## 345 1
## 351 1
## 353 0
## 356 1
## 360 1
## 361 1
## 363 1
## 367 0
## 369 1
## 372 0
## 375 1
## 376 1
## 383 0
## 385 0
## 408 0
## 410 0
## 411 1
## 423 1
## 425 0
## 427 1
## 432 0
## 434 1
## 436 1
## 439 1
## 444 1
## 453 0
## 454 1
## 460 1
## 470 0
## 471 1
## 484 1
## 487 0
## 488 0
## 495 1
## 496 1
## 497 0
## 501 1
## 506 1
## 507 0
## 513 0
## 515 1
## 520 0
## 521 0
## 525 0
## 530 1
## 532 1
## 535 1
## 536 1
## 546 0
## 550 1
## 552 0
## 556 0
## 558 1
## 561 1
## 565 0
## 571 1
## 572 0
## 576 1
## 583 1
## 584 0
## 587 0
## 591 1
## 592 0
## 596 0
## 600 1
## 607 0
## 612 0
## 620 0
## 624 0
## 629 1
## 635 0
## 640 1
## 641 1
## 659 0
## 662 0
## 667 1
## 681 1
## 687 0
## 689 0
## 702 0
## 713 1
## 716 1
## 717 0
## 723 1
## 725 1
## 728 1
## 730 1
## 734 1
## 740 0
## 742 1
## 743 0
## 748 0
## 750 1
## 760 0
## 777 0
## 782 0
## 790 0
## 795 0
## 801 0
## 805 1
## 810 1
## 812 0
## 825 0
## 845 0
## 846 0
## 848 0
## 849 1
## 852 1
## 863 0
## 868 1
## 884 0
## 886 0
## 889 0
## 894 0
## 900 1
## 901 0
## 904 0
## 918 1
## 930 0
## 933 1
## 941 0
## 948 1
## 952 0
## 961 1
## 969 0
## 974 1
## 984 1
## 991 1
## 995 0
## 996 1
## 1002 1
## 1011 0
## 1014 0
## 1018 0
## 1022 0entrenamiento$target <- as.factor(entrenamiento$target)
prueba$target <- as.factor(prueba$target)
modelo1 <- train(target ~ ., data=entrenamiento,
method = "svmLinear",
preProcess=c("scale", "center"),
trControl = trainControl(method="cv", number=10),
tuneGrid = data.frame(C=1)
)
resultado_entrenamiento1 <- predict(modelo1, entrenamiento)
resultado_prueba1 <- predict(modelo1, prueba)
resultado_entrenamiento1 <- factor(resultado_entrenamiento1, levels = levels(entrenamiento$target))
resultado_prueba1 <- factor(resultado_prueba1, levels = levels(prueba$target))
mcre1 <- confusionMatrix(resultado_entrenamiento1, entrenamiento$target)
mcrp1 <- confusionMatrix(resultado_prueba1, prueba$target)
print(mcre1)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 315 40
## 1 89 376
##
## Accuracy : 0.8427
## 95% CI : (0.8159, 0.8669)
## No Information Rate : 0.5073
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.6847
##
## Mcnemar's Test P-Value : 2.377e-05
##
## Sensitivity : 0.7797
## Specificity : 0.9038
## Pos Pred Value : 0.8873
## Neg Pred Value : 0.8086
## Prevalence : 0.4927
## Detection Rate : 0.3841
## Detection Prevalence : 0.4329
## Balanced Accuracy : 0.8418
##
## 'Positive' Class : 0
## print(mcrp1)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 74 7
## 1 21 103
##
## Accuracy : 0.8634
## 95% CI : (0.8087, 0.9073)
## No Information Rate : 0.5366
## P-Value [Acc > NIR] : < 2e-16
##
## Kappa : 0.7226
##
## Mcnemar's Test P-Value : 0.01402
##
## Sensitivity : 0.7789
## Specificity : 0.9364
## Pos Pred Value : 0.9136
## Neg Pred Value : 0.8306
## Prevalence : 0.4634
## Detection Rate : 0.3610
## Detection Prevalence : 0.3951
## Balanced Accuracy : 0.8577
##
## 'Positive' Class : 0
## modelo2 <- train(target ~ ., data=entrenamiento,
method = "svmRadial",
preProcess=c("scale","center"),
trControl = trainControl(method="cv", number=10),
tuneGrid = data.frame(sigma=1, C=1)
)
resultado_entrenamiento2 <- predict(modelo2, entrenamiento)
resultado_prueba2 <- predict(modelo2, prueba)
mcre2 <- confusionMatrix(resultado_entrenamiento2, entrenamiento$target)
mcrp2 <- confusionMatrix(resultado_prueba2, prueba$target)modelo3 <- train(target ~ ., data=entrenamiento,
method = "svmPoly",
preProcess=c("scale","center"),
trControl = trainControl(method="cv", number=10),
tuneGrid = data.frame(degree=1, scale=1, C=1)
)
resultado_entrenamiento3 <- predict(modelo3, entrenamiento)
resultado_prueba3 <- predict(modelo3, prueba)
mcre3 <- confusionMatrix(resultado_entrenamiento3, entrenamiento$target)
mcrp3 <- confusionMatrix(resultado_prueba3, prueba$target)modelo4 <- train(target ~ ., data=entrenamiento,
method = "rpart",
preProcess=c("scale","center"),
trControl = trainControl(method="cv", number=10),
tuneLength = 10
)
resultado_entrenamiento4 <- predict(modelo4, entrenamiento)
resultado_prueba4 <- predict(modelo4, prueba)
mcre4 <- confusionMatrix(resultado_entrenamiento4, entrenamiento$target)
mcrp4 <- confusionMatrix(resultado_prueba4, prueba$target)modelo5 <- train(target ~ ., data=entrenamiento,
method = "nnet",
preProcess=c("scale","center"),
trControl = trainControl(method="cv", number=10)
)## # weights: 16
## initial value 501.608993
## iter 10 value 303.085280
## iter 20 value 277.738916
## iter 30 value 272.091590
## iter 40 value 271.238055
## iter 50 value 270.564656
## iter 60 value 269.629343
## iter 70 value 269.490466
## iter 80 value 269.467957
## iter 90 value 269.385196
## iter 100 value 269.327539
## final value 269.327539
## stopped after 100 iterations
## # weights: 46
## initial value 522.574867
## iter 10 value 261.487478
## iter 20 value 223.030376
## iter 30 value 181.751492
## iter 40 value 164.791099
## iter 50 value 155.182052
## iter 60 value 146.208766
## iter 70 value 145.049097
## iter 80 value 144.791929
## iter 90 value 144.789494
## final value 144.789335
## converged
## # weights: 76
## initial value 515.705782
## iter 10 value 238.097498
## iter 20 value 171.547977
## iter 30 value 128.416301
## iter 40 value 109.536288
## iter 50 value 103.478559
## iter 60 value 96.202672
## iter 70 value 87.745629
## iter 80 value 85.977242
## iter 90 value 84.988532
## iter 100 value 83.950081
## final value 83.950081
## stopped after 100 iterations
## # weights: 16
## initial value 531.174394
## iter 10 value 313.563438
## iter 20 value 302.962146
## iter 30 value 291.708660
## iter 40 value 285.287134
## iter 50 value 284.651741
## iter 60 value 283.160089
## iter 70 value 276.678630
## iter 80 value 276.070539
## iter 90 value 270.693517
## iter 100 value 270.603041
## final value 270.603041
## stopped after 100 iterations
## # weights: 46
## initial value 490.015192
## iter 10 value 254.361668
## iter 20 value 230.979633
## iter 30 value 228.258661
## iter 40 value 223.486663
## iter 50 value 214.646694
## iter 60 value 212.281869
## iter 70 value 212.065944
## final value 212.065895
## converged
## # weights: 76
## initial value 560.862977
## iter 10 value 242.207686
## iter 20 value 201.684943
## iter 30 value 179.253427
## iter 40 value 170.791644
## iter 50 value 169.593790
## iter 60 value 165.898865
## iter 70 value 150.710357
## iter 80 value 134.135979
## iter 90 value 125.095224
## iter 100 value 122.576215
## final value 122.576215
## stopped after 100 iterations
## # weights: 16
## initial value 541.567609
## iter 10 value 364.939132
## iter 20 value 300.893875
## iter 30 value 277.272464
## iter 40 value 276.348114
## iter 50 value 263.044337
## iter 60 value 256.447442
## iter 70 value 256.196400
## iter 80 value 256.138058
## final value 256.136420
## converged
## # weights: 46
## initial value 485.740742
## iter 10 value 246.991499
## iter 20 value 188.337422
## iter 30 value 160.040470
## iter 40 value 144.588525
## iter 50 value 140.045363
## iter 60 value 138.965138
## iter 70 value 138.664846
## iter 80 value 138.160641
## iter 90 value 137.895869
## iter 100 value 137.720669
## final value 137.720669
## stopped after 100 iterations
## # weights: 76
## initial value 494.813660
## iter 10 value 240.353424
## iter 20 value 148.781908
## iter 30 value 99.000581
## iter 40 value 87.433815
## iter 50 value 85.246637
## iter 60 value 82.651624
## iter 70 value 80.449133
## iter 80 value 79.482379
## iter 90 value 79.264913
## iter 100 value 79.139853
## final value 79.139853
## stopped after 100 iterations
## # weights: 16
## initial value 495.761239
## iter 10 value 262.743205
## iter 20 value 255.589292
## iter 30 value 244.752320
## iter 40 value 244.172038
## iter 50 value 244.166418
## iter 60 value 244.164991
## final value 244.164722
## converged
## # weights: 46
## initial value 549.470350
## iter 10 value 232.885800
## iter 20 value 182.512277
## iter 30 value 169.197290
## iter 40 value 154.745400
## iter 50 value 141.323782
## iter 60 value 139.565730
## iter 70 value 139.538871
## final value 139.538516
## converged
## # weights: 76
## initial value 573.327113
## iter 10 value 197.717940
## iter 20 value 112.743469
## iter 30 value 74.105233
## iter 40 value 53.620146
## iter 50 value 49.458499
## iter 60 value 47.822150
## iter 70 value 47.623889
## iter 80 value 47.604862
## iter 90 value 47.596031
## iter 100 value 47.586046
## final value 47.586046
## stopped after 100 iterations
## # weights: 16
## initial value 494.520214
## iter 10 value 307.466497
## iter 20 value 272.033981
## iter 30 value 270.769478
## iter 40 value 270.049326
## iter 50 value 269.983242
## final value 269.982362
## converged
## # weights: 46
## initial value 523.919817
## iter 10 value 278.136113
## iter 20 value 264.054922
## iter 30 value 253.136670
## iter 40 value 221.105634
## iter 50 value 209.316345
## iter 60 value 193.542004
## iter 70 value 189.785814
## iter 80 value 181.330790
## iter 90 value 178.765726
## iter 100 value 178.694758
## final value 178.694758
## stopped after 100 iterations
## # weights: 76
## initial value 458.163126
## iter 10 value 239.738804
## iter 20 value 190.112595
## iter 30 value 157.003826
## iter 40 value 144.671150
## iter 50 value 140.439213
## iter 60 value 137.776009
## iter 70 value 130.061931
## iter 80 value 128.170257
## iter 90 value 128.013517
## iter 100 value 127.961233
## final value 127.961233
## stopped after 100 iterations
## # weights: 16
## initial value 558.127915
## iter 10 value 328.458226
## iter 20 value 286.635176
## iter 30 value 278.940736
## iter 40 value 272.009299
## iter 50 value 269.142302
## iter 60 value 267.606628
## iter 70 value 267.592332
## iter 80 value 267.587074
## iter 90 value 267.586641
## final value 267.586624
## converged
## # weights: 46
## initial value 578.431238
## iter 10 value 233.234118
## iter 20 value 201.629784
## iter 30 value 185.850659
## iter 40 value 160.932787
## iter 50 value 154.800125
## iter 60 value 153.941052
## iter 70 value 153.435193
## iter 80 value 152.980896
## iter 90 value 152.581699
## iter 100 value 151.996075
## final value 151.996075
## stopped after 100 iterations
## # weights: 76
## initial value 551.427635
## iter 10 value 212.817771
## iter 20 value 130.369270
## iter 30 value 108.423394
## iter 40 value 101.183420
## iter 50 value 93.156118
## iter 60 value 92.203352
## iter 70 value 92.060260
## iter 80 value 91.999124
## iter 90 value 91.873651
## iter 100 value 91.561444
## final value 91.561444
## stopped after 100 iterations
## # weights: 16
## initial value 499.717620
## iter 10 value 305.175224
## iter 20 value 280.639642
## iter 30 value 269.887436
## iter 40 value 267.369359
## iter 50 value 265.618588
## iter 60 value 265.284730
## iter 70 value 265.238428
## iter 80 value 265.086833
## iter 90 value 264.947294
## iter 100 value 264.944085
## final value 264.944085
## stopped after 100 iterations
## # weights: 46
## initial value 561.612701
## iter 10 value 256.676245
## iter 20 value 189.171312
## iter 30 value 167.674197
## iter 40 value 155.017446
## iter 50 value 144.744988
## iter 60 value 134.109536
## iter 70 value 133.005718
## iter 80 value 133.002112
## iter 90 value 132.962268
## iter 100 value 132.894606
## final value 132.894606
## stopped after 100 iterations
## # weights: 76
## initial value 521.378929
## iter 10 value 231.913732
## iter 20 value 146.133755
## iter 30 value 101.598923
## iter 40 value 90.261559
## iter 50 value 86.114038
## iter 60 value 84.562705
## iter 70 value 84.316777
## iter 80 value 83.709395
## iter 90 value 83.577937
## iter 100 value 83.479400
## final value 83.479400
## stopped after 100 iterations
## # weights: 16
## initial value 550.592788
## iter 10 value 283.628272
## iter 20 value 276.008886
## iter 30 value 272.291056
## iter 40 value 271.735169
## iter 50 value 270.876231
## iter 60 value 270.189251
## iter 60 value 270.189249
## iter 60 value 270.189249
## final value 270.189249
## converged
## # weights: 46
## initial value 545.898892
## iter 10 value 272.238053
## iter 20 value 235.052743
## iter 30 value 209.912873
## iter 40 value 199.279654
## iter 50 value 197.077568
## iter 60 value 196.386769
## iter 70 value 196.256589
## iter 80 value 196.172734
## iter 90 value 196.159338
## final value 196.159220
## converged
## # weights: 76
## initial value 501.713568
## iter 10 value 236.954878
## iter 20 value 192.909041
## iter 30 value 166.180582
## iter 40 value 149.755980
## iter 50 value 136.923777
## iter 60 value 131.947677
## iter 70 value 130.486909
## iter 80 value 128.228701
## iter 90 value 126.193458
## iter 100 value 126.053806
## final value 126.053806
## stopped after 100 iterations
## # weights: 16
## initial value 519.070143
## iter 10 value 349.702591
## iter 20 value 332.508332
## iter 30 value 304.832778
## iter 40 value 287.463900
## iter 50 value 277.033136
## iter 60 value 276.058460
## iter 70 value 275.855856
## iter 80 value 275.665237
## iter 90 value 275.661282
## iter 100 value 275.658151
## final value 275.658151
## stopped after 100 iterations
## # weights: 46
## initial value 514.240160
## iter 10 value 234.974383
## iter 20 value 193.149227
## iter 30 value 174.450760
## iter 40 value 159.540499
## iter 50 value 149.243329
## iter 60 value 146.912170
## iter 70 value 143.659613
## iter 80 value 142.109491
## iter 90 value 141.606698
## iter 100 value 141.094642
## final value 141.094642
## stopped after 100 iterations
## # weights: 76
## initial value 545.282034
## iter 10 value 207.875500
## iter 20 value 134.309701
## iter 30 value 104.909072
## iter 40 value 78.748709
## iter 50 value 68.492524
## iter 60 value 63.995719
## iter 70 value 63.533258
## iter 80 value 63.117021
## iter 90 value 62.068036
## iter 100 value 61.259225
## final value 61.259225
## stopped after 100 iterations
## # weights: 16
## initial value 508.419251
## iter 10 value 278.269246
## iter 20 value 255.811002
## iter 30 value 245.202308
## iter 40 value 235.841740
## iter 50 value 235.273145
## iter 60 value 235.197731
## iter 70 value 235.196577
## iter 80 value 235.194069
## iter 90 value 235.186062
## iter 100 value 235.184600
## final value 235.184600
## stopped after 100 iterations
## # weights: 46
## initial value 667.458053
## iter 10 value 264.359496
## iter 20 value 200.104663
## iter 30 value 167.500939
## iter 40 value 149.822138
## iter 50 value 139.339313
## iter 60 value 136.433407
## iter 70 value 124.430878
## iter 80 value 119.842482
## iter 90 value 119.190021
## iter 100 value 118.860959
## final value 118.860959
## stopped after 100 iterations
## # weights: 76
## initial value 507.148145
## iter 10 value 242.786954
## iter 20 value 151.321551
## iter 30 value 123.495642
## iter 40 value 109.433714
## iter 50 value 104.684851
## iter 60 value 102.166042
## iter 70 value 101.613127
## iter 80 value 101.505343
## iter 90 value 101.496326
## iter 100 value 101.493958
## final value 101.493958
## stopped after 100 iterations
## # weights: 16
## initial value 532.389387
## iter 10 value 353.868341
## iter 20 value 275.603309
## iter 30 value 271.944298
## iter 40 value 267.757128
## iter 50 value 267.041263
## iter 60 value 266.546117
## iter 70 value 266.475086
## iter 70 value 266.475083
## iter 70 value 266.475083
## final value 266.475083
## converged
## # weights: 46
## initial value 522.462882
## iter 10 value 256.434827
## iter 20 value 218.706181
## iter 30 value 211.613251
## iter 40 value 192.399316
## iter 50 value 172.793734
## iter 60 value 168.575534
## iter 70 value 166.867637
## iter 80 value 166.800857
## final value 166.800816
## converged
## # weights: 76
## initial value 518.440362
## iter 10 value 227.693395
## iter 20 value 182.314629
## iter 30 value 168.257957
## iter 40 value 159.775336
## iter 50 value 154.329105
## iter 60 value 150.865413
## iter 70 value 146.753316
## iter 80 value 144.983921
## iter 90 value 143.158478
## iter 100 value 142.376788
## final value 142.376788
## stopped after 100 iterations
## # weights: 16
## initial value 537.949737
## iter 10 value 352.037745
## iter 20 value 295.654814
## iter 30 value 286.566682
## iter 40 value 280.409892
## iter 50 value 278.531947
## iter 60 value 273.839553
## iter 70 value 273.697034
## iter 80 value 273.688851
## iter 90 value 273.685812
## iter 100 value 273.683674
## final value 273.683674
## stopped after 100 iterations
## # weights: 46
## initial value 618.006240
## iter 10 value 284.116794
## iter 20 value 239.147319
## iter 30 value 206.487305
## iter 40 value 180.093008
## iter 50 value 168.156632
## iter 60 value 165.715658
## iter 70 value 164.471156
## iter 80 value 163.482066
## iter 90 value 162.989076
## iter 100 value 162.450097
## final value 162.450097
## stopped after 100 iterations
## # weights: 76
## initial value 561.243753
## iter 10 value 243.164396
## iter 20 value 161.181030
## iter 30 value 127.094391
## iter 40 value 102.449338
## iter 50 value 91.731750
## iter 60 value 89.376930
## iter 70 value 88.457283
## iter 80 value 87.868953
## iter 90 value 87.485004
## iter 100 value 87.387064
## final value 87.387064
## stopped after 100 iterations
## # weights: 16
## initial value 548.364764
## iter 10 value 331.113275
## iter 20 value 271.780231
## iter 30 value 260.582785
## iter 40 value 258.095663
## iter 50 value 250.300608
## iter 60 value 233.319191
## final value 233.192830
## converged
## # weights: 46
## initial value 625.997743
## iter 10 value 259.610999
## iter 20 value 211.051686
## iter 30 value 177.160554
## iter 40 value 161.058387
## iter 50 value 152.615103
## iter 60 value 152.327742
## iter 70 value 152.326413
## final value 152.326292
## converged
## # weights: 76
## initial value 575.392742
## iter 10 value 212.489967
## iter 20 value 153.875648
## iter 30 value 105.269151
## iter 40 value 93.446827
## iter 50 value 86.100233
## iter 60 value 74.384193
## iter 70 value 67.247274
## iter 80 value 62.649670
## iter 90 value 56.829710
## iter 100 value 51.906438
## final value 51.906438
## stopped after 100 iterations
## # weights: 16
## initial value 546.390766
## iter 10 value 287.731739
## iter 20 value 276.460393
## iter 30 value 272.567094
## iter 40 value 271.998637
## iter 50 value 271.763684
## final value 271.737911
## converged
## # weights: 46
## initial value 537.656495
## iter 10 value 239.256594
## iter 20 value 206.573544
## iter 30 value 198.329741
## iter 40 value 188.771717
## iter 50 value 176.918623
## iter 60 value 168.779178
## iter 70 value 166.077115
## iter 80 value 165.972312
## iter 90 value 165.959665
## final value 165.959586
## converged
## # weights: 76
## initial value 631.896899
## iter 10 value 270.034581
## iter 20 value 216.612174
## iter 30 value 191.020909
## iter 40 value 177.529454
## iter 50 value 171.884915
## iter 60 value 169.474284
## iter 70 value 168.557214
## iter 80 value 168.064472
## iter 90 value 167.396426
## iter 100 value 165.234901
## final value 165.234901
## stopped after 100 iterations
## # weights: 16
## initial value 518.934268
## iter 10 value 295.265851
## iter 20 value 263.133649
## iter 30 value 258.419928
## iter 40 value 250.126210
## iter 50 value 246.120392
## iter 60 value 244.391969
## iter 70 value 244.264271
## iter 80 value 244.257460
## iter 90 value 244.254647
## iter 100 value 244.254344
## final value 244.254344
## stopped after 100 iterations
## # weights: 46
## initial value 478.753391
## iter 10 value 271.866491
## iter 20 value 228.085287
## iter 30 value 197.628652
## iter 40 value 176.701806
## iter 50 value 174.130542
## iter 60 value 171.530304
## iter 70 value 170.968224
## iter 80 value 170.869405
## iter 90 value 170.686598
## iter 100 value 170.494102
## final value 170.494102
## stopped after 100 iterations
## # weights: 76
## initial value 586.141179
## iter 10 value 252.114678
## iter 20 value 186.394151
## iter 30 value 157.575027
## iter 40 value 133.202021
## iter 50 value 115.102382
## iter 60 value 108.100839
## iter 70 value 106.574854
## iter 80 value 105.814557
## iter 90 value 105.302467
## iter 100 value 102.953977
## final value 102.953977
## stopped after 100 iterations
## # weights: 16
## initial value 547.723145
## iter 10 value 277.870791
## iter 20 value 268.523595
## iter 30 value 267.383603
## iter 40 value 266.924902
## iter 50 value 266.214193
## iter 60 value 265.136303
## iter 70 value 264.978996
## iter 80 value 264.918352
## iter 90 value 264.776059
## iter 100 value 264.714876
## final value 264.714876
## stopped after 100 iterations
## # weights: 46
## initial value 486.170215
## iter 10 value 224.840397
## iter 20 value 193.701497
## iter 30 value 168.511636
## iter 40 value 154.990536
## iter 50 value 151.483646
## iter 60 value 148.745970
## iter 70 value 146.334625
## iter 80 value 144.417047
## iter 90 value 142.657270
## iter 100 value 141.962479
## final value 141.962479
## stopped after 100 iterations
## # weights: 76
## initial value 561.225487
## iter 10 value 233.836964
## iter 20 value 137.862589
## iter 30 value 100.707660
## iter 40 value 87.280258
## iter 50 value 82.372126
## iter 60 value 79.965188
## iter 70 value 78.970954
## iter 80 value 78.849107
## iter 90 value 78.833768
## iter 100 value 78.831650
## final value 78.831650
## stopped after 100 iterations
## # weights: 16
## initial value 499.794993
## iter 10 value 297.618560
## iter 20 value 273.832188
## iter 30 value 268.410128
## iter 40 value 268.347593
## final value 268.347582
## converged
## # weights: 46
## initial value 518.826719
## iter 10 value 246.808813
## iter 20 value 236.849143
## iter 30 value 200.394083
## iter 40 value 189.123940
## iter 50 value 187.903610
## iter 60 value 187.423673
## iter 70 value 187.161980
## iter 80 value 187.159754
## iter 80 value 187.159753
## iter 80 value 187.159752
## final value 187.159752
## converged
## # weights: 76
## initial value 553.004921
## iter 10 value 236.855956
## iter 20 value 183.916607
## iter 30 value 165.690450
## iter 40 value 148.804191
## iter 50 value 138.960302
## iter 60 value 135.569243
## iter 70 value 134.309488
## iter 80 value 132.837211
## iter 90 value 132.094023
## iter 100 value 131.978105
## final value 131.978105
## stopped after 100 iterations
## # weights: 16
## initial value 517.537021
## iter 10 value 261.511034
## iter 20 value 252.936656
## iter 30 value 250.754826
## iter 40 value 243.009847
## iter 50 value 241.101617
## iter 60 value 240.122647
## iter 70 value 239.768211
## iter 80 value 239.757244
## iter 90 value 239.755256
## iter 100 value 239.753417
## final value 239.753417
## stopped after 100 iterations
## # weights: 46
## initial value 562.195937
## iter 10 value 294.555121
## iter 20 value 243.197868
## iter 30 value 224.058670
## iter 40 value 215.154644
## iter 50 value 201.016621
## iter 60 value 191.879932
## iter 70 value 182.898143
## iter 80 value 181.076767
## iter 90 value 180.952183
## iter 100 value 180.488581
## final value 180.488581
## stopped after 100 iterations
## # weights: 76
## initial value 535.988042
## iter 10 value 198.997262
## iter 20 value 127.844150
## iter 30 value 93.896868
## iter 40 value 73.722722
## iter 50 value 66.719004
## iter 60 value 62.970285
## iter 70 value 59.314578
## iter 80 value 58.705962
## iter 90 value 58.438658
## iter 100 value 58.315681
## final value 58.315681
## stopped after 100 iterations
## # weights: 16
## initial value 523.919988
## iter 10 value 351.344657
## iter 20 value 269.278258
## iter 30 value 267.724626
## iter 40 value 267.097790
## iter 50 value 266.949508
## iter 60 value 266.586505
## iter 70 value 266.478816
## iter 80 value 266.469723
## iter 90 value 266.439708
## iter 100 value 266.382949
## final value 266.382949
## stopped after 100 iterations
## # weights: 46
## initial value 530.848696
## iter 10 value 262.219044
## iter 20 value 214.659376
## iter 30 value 182.470731
## iter 40 value 158.313032
## iter 50 value 124.892579
## iter 60 value 107.816408
## iter 70 value 105.871139
## iter 80 value 105.516487
## iter 90 value 105.425752
## iter 100 value 105.392620
## final value 105.392620
## stopped after 100 iterations
## # weights: 76
## initial value 572.283358
## iter 10 value 246.627413
## iter 20 value 196.517003
## iter 30 value 104.880863
## iter 40 value 81.237477
## iter 50 value 76.907629
## iter 60 value 74.481730
## iter 70 value 72.031626
## iter 80 value 65.753754
## iter 90 value 65.064300
## iter 100 value 65.003109
## final value 65.003109
## stopped after 100 iterations
## # weights: 16
## initial value 523.788681
## iter 10 value 288.412022
## iter 20 value 275.024141
## iter 30 value 273.266281
## final value 273.264648
## converged
## # weights: 46
## initial value 519.927750
## iter 10 value 280.643271
## iter 20 value 248.835772
## iter 30 value 222.731065
## iter 40 value 217.707456
## iter 50 value 205.272451
## iter 60 value 200.380015
## iter 70 value 197.195682
## iter 80 value 196.803246
## iter 90 value 196.795646
## final value 196.795607
## converged
## # weights: 76
## initial value 514.204376
## iter 10 value 247.422184
## iter 20 value 196.162391
## iter 30 value 158.978933
## iter 40 value 145.385683
## iter 50 value 139.489708
## iter 60 value 137.068384
## iter 70 value 135.094972
## iter 80 value 134.633127
## iter 90 value 134.248488
## iter 100 value 133.433725
## final value 133.433725
## stopped after 100 iterations
## # weights: 16
## initial value 523.018837
## iter 10 value 328.439989
## iter 20 value 275.692345
## iter 30 value 269.276793
## iter 40 value 268.434512
## iter 50 value 266.784920
## iter 60 value 260.294871
## iter 70 value 255.547312
## iter 80 value 249.739257
## iter 90 value 249.065001
## iter 100 value 248.595532
## final value 248.595532
## stopped after 100 iterations
## # weights: 46
## initial value 562.711421
## iter 10 value 249.121632
## iter 20 value 208.114792
## iter 30 value 185.389052
## iter 40 value 172.443420
## iter 50 value 157.307223
## iter 60 value 156.598485
## iter 70 value 156.337113
## iter 80 value 156.169520
## iter 90 value 155.928760
## iter 100 value 155.844542
## final value 155.844542
## stopped after 100 iterations
## # weights: 76
## initial value 480.930030
## iter 10 value 237.567284
## iter 20 value 154.299590
## iter 30 value 119.811674
## iter 40 value 102.237661
## iter 50 value 99.108931
## iter 60 value 98.836572
## iter 70 value 98.713650
## iter 80 value 98.662764
## iter 90 value 98.575091
## iter 100 value 98.522810
## final value 98.522810
## stopped after 100 iterations
## # weights: 16
## initial value 548.601740
## iter 10 value 335.559247
## iter 20 value 282.085469
## iter 30 value 279.100390
## iter 40 value 278.883847
## iter 50 value 277.968075
## iter 60 value 276.448429
## iter 70 value 276.096736
## iter 80 value 276.066943
## iter 90 value 275.828427
## iter 100 value 275.737719
## final value 275.737719
## stopped after 100 iterations
## # weights: 46
## initial value 502.546361
## iter 10 value 268.212459
## iter 20 value 221.323931
## iter 30 value 193.439436
## iter 40 value 186.862433
## iter 50 value 180.978607
## iter 60 value 175.852463
## iter 70 value 175.690710
## final value 175.690397
## converged
## # weights: 76
## initial value 582.707803
## iter 10 value 266.889034
## iter 20 value 187.334196
## iter 30 value 124.428001
## iter 40 value 96.210153
## iter 50 value 86.419860
## iter 60 value 83.635621
## iter 70 value 82.097752
## iter 80 value 81.433833
## iter 90 value 81.070809
## iter 100 value 80.944839
## final value 80.944839
## stopped after 100 iterations
## # weights: 16
## initial value 514.084721
## iter 10 value 365.451803
## iter 20 value 283.401442
## iter 30 value 280.218758
## iter 40 value 280.193282
## final value 280.193238
## converged
## # weights: 46
## initial value 539.264469
## iter 10 value 291.860084
## iter 20 value 253.814758
## iter 30 value 231.480194
## iter 40 value 225.959467
## iter 50 value 220.846829
## iter 60 value 212.093395
## iter 70 value 207.059926
## iter 80 value 206.819349
## iter 90 value 205.284331
## iter 100 value 204.054152
## final value 204.054152
## stopped after 100 iterations
## # weights: 76
## initial value 489.361337
## iter 10 value 237.736287
## iter 20 value 197.194673
## iter 30 value 180.001210
## iter 40 value 162.651827
## iter 50 value 141.310462
## iter 60 value 133.848915
## iter 70 value 131.194976
## iter 80 value 127.235640
## iter 90 value 125.578180
## iter 100 value 125.350777
## final value 125.350777
## stopped after 100 iterations
## # weights: 16
## initial value 518.246253
## iter 10 value 313.361249
## iter 20 value 311.198385
## iter 30 value 301.499581
## iter 40 value 289.664903
## iter 50 value 284.817531
## iter 60 value 284.613326
## iter 70 value 284.603610
## iter 70 value 284.603610
## final value 284.603610
## converged
## # weights: 46
## initial value 563.000810
## iter 10 value 250.797083
## iter 20 value 181.876400
## iter 30 value 152.505346
## iter 40 value 136.981640
## iter 50 value 128.895413
## iter 60 value 121.583731
## iter 70 value 120.252337
## iter 80 value 114.729778
## iter 90 value 111.494676
## iter 100 value 110.288718
## final value 110.288718
## stopped after 100 iterations
## # weights: 76
## initial value 538.891637
## iter 10 value 227.115987
## iter 20 value 134.320227
## iter 30 value 98.211881
## iter 40 value 84.544035
## iter 50 value 76.193597
## iter 60 value 70.798072
## iter 70 value 67.118037
## iter 80 value 65.128391
## iter 90 value 62.693899
## iter 100 value 61.328871
## final value 61.328871
## stopped after 100 iterations
## # weights: 16
## initial value 504.556441
## iter 10 value 335.405686
## iter 20 value 258.595702
## iter 30 value 249.392420
## iter 40 value 242.803492
## iter 50 value 240.626619
## iter 60 value 239.536576
## iter 70 value 238.813383
## iter 80 value 238.769494
## iter 90 value 238.759788
## iter 100 value 238.756445
## final value 238.756445
## stopped after 100 iterations
## # weights: 46
## initial value 633.956374
## iter 10 value 231.116788
## iter 20 value 164.915094
## iter 30 value 136.105833
## iter 40 value 124.213013
## iter 50 value 112.857306
## iter 60 value 111.058617
## iter 70 value 111.032545
## iter 80 value 111.031827
## iter 80 value 111.031825
## iter 80 value 111.031825
## final value 111.031825
## converged
## # weights: 76
## initial value 559.142983
## iter 10 value 242.710702
## iter 20 value 196.433817
## iter 30 value 161.623756
## iter 40 value 132.858058
## iter 50 value 117.075351
## iter 60 value 111.009350
## iter 70 value 106.345763
## iter 80 value 102.549296
## iter 90 value 99.335320
## iter 100 value 97.179622
## final value 97.179622
## stopped after 100 iterations
## # weights: 16
## initial value 544.580369
## iter 10 value 273.296518
## iter 20 value 264.077639
## final value 263.113587
## converged
## # weights: 46
## initial value 523.677517
## iter 10 value 273.659617
## iter 20 value 230.387658
## iter 30 value 200.632895
## iter 40 value 193.757968
## iter 50 value 192.773264
## iter 60 value 192.743761
## final value 192.743226
## converged
## # weights: 76
## initial value 569.418018
## iter 10 value 248.071004
## iter 20 value 203.029706
## iter 30 value 181.451649
## iter 40 value 169.946339
## iter 50 value 163.182653
## iter 60 value 160.676763
## iter 70 value 159.595647
## iter 80 value 156.538324
## iter 90 value 152.711105
## iter 100 value 151.698206
## final value 151.698206
## stopped after 100 iterations
## # weights: 16
## initial value 498.690624
## iter 10 value 318.273208
## iter 20 value 271.108243
## iter 30 value 263.076172
## iter 40 value 262.397643
## iter 50 value 262.327191
## iter 60 value 262.071015
## iter 70 value 262.009289
## iter 80 value 262.009028
## iter 90 value 262.000658
## final value 262.000650
## converged
## # weights: 46
## initial value 524.723394
## iter 10 value 264.264776
## iter 20 value 216.490973
## iter 30 value 202.711733
## iter 40 value 183.975125
## iter 50 value 172.943086
## iter 60 value 172.349378
## iter 70 value 172.223302
## iter 80 value 172.114440
## iter 90 value 171.315112
## iter 100 value 171.247567
## final value 171.247567
## stopped after 100 iterations
## # weights: 76
## initial value 597.243127
## iter 10 value 223.877466
## iter 20 value 142.690153
## iter 30 value 103.163597
## iter 40 value 97.207584
## iter 50 value 93.667068
## iter 60 value 92.363944
## iter 70 value 92.063823
## iter 80 value 91.954566
## iter 90 value 91.888477
## iter 100 value 91.815104
## final value 91.815104
## stopped after 100 iterations
## # weights: 16
## initial value 521.206815
## iter 10 value 440.124809
## iter 20 value 350.856317
## iter 30 value 277.027921
## iter 40 value 271.577789
## iter 50 value 270.506956
## iter 60 value 269.967641
## iter 70 value 269.841408
## iter 80 value 269.834155
## iter 90 value 269.802176
## iter 100 value 269.763858
## final value 269.763858
## stopped after 100 iterations
## # weights: 46
## initial value 594.154779
## iter 10 value 250.404211
## iter 20 value 217.172871
## iter 30 value 165.101773
## iter 40 value 149.192675
## iter 50 value 140.281429
## iter 60 value 138.592852
## iter 70 value 138.580254
## final value 138.580240
## converged
## # weights: 76
## initial value 592.135411
## iter 10 value 246.757858
## iter 20 value 163.005045
## iter 30 value 116.802392
## iter 40 value 105.264614
## iter 50 value 92.430188
## iter 60 value 87.557083
## iter 70 value 85.362887
## iter 80 value 84.645166
## iter 90 value 84.623077
## iter 100 value 84.614555
## final value 84.614555
## stopped after 100 iterations
## # weights: 16
## initial value 490.705547
## iter 10 value 319.703124
## iter 20 value 279.511157
## iter 30 value 271.560912
## final value 271.554651
## converged
## # weights: 46
## initial value 491.159737
## iter 10 value 256.748913
## iter 20 value 224.958815
## iter 30 value 205.055537
## iter 40 value 196.085087
## iter 50 value 194.992135
## iter 60 value 194.890753
## iter 70 value 194.880866
## final value 194.880851
## converged
## # weights: 76
## initial value 568.265619
## iter 10 value 232.001679
## iter 20 value 178.612541
## iter 30 value 156.505896
## iter 40 value 147.063278
## iter 50 value 142.379276
## iter 60 value 137.982141
## iter 70 value 133.476190
## iter 80 value 125.775175
## iter 90 value 119.958724
## iter 100 value 116.443264
## final value 116.443264
## stopped after 100 iterations
## # weights: 16
## initial value 515.685801
## iter 10 value 281.082819
## iter 20 value 272.253756
## iter 30 value 262.709792
## iter 40 value 256.726358
## iter 50 value 255.490960
## iter 60 value 250.199411
## iter 70 value 250.081791
## iter 80 value 250.080401
## iter 90 value 250.077970
## iter 100 value 250.045004
## final value 250.045004
## stopped after 100 iterations
## # weights: 46
## initial value 552.303115
## iter 10 value 291.495797
## iter 20 value 207.488503
## iter 30 value 190.457529
## iter 40 value 178.988995
## iter 50 value 175.377290
## iter 60 value 173.576882
## iter 70 value 172.736454
## iter 80 value 171.768852
## iter 90 value 171.138861
## iter 100 value 170.875667
## final value 170.875667
## stopped after 100 iterations
## # weights: 76
## initial value 501.305291
## iter 10 value 213.141130
## iter 20 value 156.703550
## iter 30 value 127.907908
## iter 40 value 113.607249
## iter 50 value 102.843034
## iter 60 value 101.102221
## iter 70 value 100.928528
## iter 80 value 100.841160
## iter 90 value 100.758529
## iter 100 value 100.669049
## final value 100.669049
## stopped after 100 iterations
## # weights: 76
## initial value 596.581003
## iter 10 value 250.647011
## iter 20 value 163.416651
## iter 30 value 112.173249
## iter 40 value 81.871766
## iter 50 value 71.970226
## iter 60 value 68.708798
## iter 70 value 68.176001
## iter 80 value 68.150074
## iter 90 value 68.145207
## iter 100 value 68.144025
## final value 68.144025
## stopped after 100 iterationsresultado_entrenamiento5 <- predict(modelo5, entrenamiento)
resultado_prueba5 <- predict(modelo5, prueba)
mcre5 <- confusionMatrix(resultado_entrenamiento5, entrenamiento$target)
mcrp5 <- confusionMatrix(resultado_prueba5, prueba$target)modelo6 <- train(target ~ ., data=entrenamiento,
method = "rf",
preProcess=c("scale","center"),
trControl = trainControl(method="cv", number=10),
tuneGrid = expand.grid(mtry = c(2,4,6))
)
resultado_entrenamiento6 <- predict(modelo6, entrenamiento)
resultado_prueba6 <- predict(modelo6, prueba)
mcre6 <- confusionMatrix(resultado_entrenamiento6, entrenamiento$target)
mcrp6 <- confusionMatrix(resultado_prueba6, prueba$target)resultados <- data.frame(
"svmLinear" =c(mcre1$overall["Accuracy"], mcrp1$overall["Accuracy"]),
"svmRadial" =c(mcre2$overall["Accuracy"], mcrp2$overall["Accuracy"]),
"svmPoly" =c(mcre3$overall["Accuracy"], mcrp3$overall["Accuracy"]),
"rpart" =c(mcre4$overall["Accuracy"], mcrp4$overall["Accuracy"]),
"nnet" =c(mcre5$overall["Accuracy"], mcrp5$overall["Accuracy"]),
"rf" =c(mcre6$overall["Accuracy"], mcrp6$overall["Accuracy"])
)
rownames(resultados) <- c("Precision de Entrenamiento", "Precision de Prueba")
resultados## svmLinear svmRadial svmPoly rpart nnet rf
## Precision de Entrenamiento 0.8426829 1 0.8426829 0.9012195 0.9719512 1
## Precision de Prueba 0.8634146 1 0.8634146 0.8634146 0.9609756 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