CARET - Heart disease
Mariana Garcia A01177705
2024-08-21
Teoria
El paquete CARET (Clafication and Regression Training) es un paquete integral con una amplia variedad de algoritmos para el; aprendizaje automatico.
Instalar paquetes y llamar librerias
#install.packages("ggplot2")
library(ggplot2) #Graficas con mejor diseno
#install.packages("lattice")
library(lattice) #Crear graficos
#install.packages("caret")
library(caret) #Algoritmos de aprendizaje automatico
#install.packages("datasets")
library(datasets) #Usar la base de datos "Iris"
#install.packages("DataExplorer")
library(DataExplorer) #Exploracion de datos
#install.packages("kernlab")
library(kernlab) #Metodos de aprendizaje automatico
#install.packages("randomForest")
library(randomForest) #Exploracion de datosCrear base de datos
df <- read.csv("C:\\Users\\maria\\OneDrive\\Desktop\\AD24\\Modulo 2\\heart.csv")Analisis Exploratorio
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)
Convertir variables a factores
df$sex <- as.factor(df$sex)
df$target <- as.factor(df$target) #variable que queremos predecir
df$cp <- as.factor(df$cp)
df$restecg <- as.factor(df$restecg)
df$ca <- as.factor(df$ca)
df$thal <- as.factor(df$thal)
df$exang <- as.factor(df$exang)
df$fbs <- as.factor(df$fbs)
df$slope <- as.factor(df$slope)
df## 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
## 2 53 1 0 140 203 1 0 155 1 3.1 0 0 3
## 3 70 1 0 145 174 0 1 125 1 2.6 0 0 3
## 4 61 1 0 148 203 0 1 161 0 0.0 2 1 3
## 5 62 0 0 138 294 1 1 106 0 1.9 1 3 2
## 6 58 0 0 100 248 0 0 122 0 1.0 1 0 2
## 7 58 1 0 114 318 0 2 140 0 4.4 0 3 1
## 8 55 1 0 160 289 0 0 145 1 0.8 1 1 3
## 9 46 1 0 120 249 0 0 144 0 0.8 2 0 3
## 10 54 1 0 122 286 0 0 116 1 3.2 1 2 2
## 11 71 0 0 112 149 0 1 125 0 1.6 1 0 2
## 12 43 0 0 132 341 1 0 136 1 3.0 1 0 3
## 13 34 0 1 118 210 0 1 192 0 0.7 2 0 2
## 14 51 1 0 140 298 0 1 122 1 4.2 1 3 3
## 15 52 1 0 128 204 1 1 156 1 1.0 1 0 0
## 16 34 0 1 118 210 0 1 192 0 0.7 2 0 2
## 17 51 0 2 140 308 0 0 142 0 1.5 2 1 2
## 18 54 1 0 124 266 0 0 109 1 2.2 1 1 3
## 19 50 0 1 120 244 0 1 162 0 1.1 2 0 2
## 20 58 1 2 140 211 1 0 165 0 0.0 2 0 2
## 21 60 1 2 140 185 0 0 155 0 3.0 1 0 2
## 22 67 0 0 106 223 0 1 142 0 0.3 2 2 2
## 23 45 1 0 104 208 0 0 148 1 3.0 1 0 2
## 24 63 0 2 135 252 0 0 172 0 0.0 2 0 2
## 25 42 0 2 120 209 0 1 173 0 0.0 1 0 2
## 26 61 0 0 145 307 0 0 146 1 1.0 1 0 3
## 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
## 29 56 1 2 130 256 1 0 142 1 0.6 1 1 1
## 30 55 0 0 180 327 0 2 117 1 3.4 1 0 2
## 31 44 1 0 120 169 0 1 144 1 2.8 0 0 1
## 32 50 0 1 120 244 0 1 162 0 1.1 2 0 2
## 33 57 1 0 130 131 0 1 115 1 1.2 1 1 3
## 34 70 1 2 160 269 0 1 112 1 2.9 1 1 3
## 35 50 1 2 129 196 0 1 163 0 0.0 2 0 2
## 36 46 1 2 150 231 0 1 147 0 3.6 1 0 2
## 37 51 1 3 125 213 0 0 125 1 1.4 2 1 2
## 38 59 1 0 138 271 0 0 182 0 0.0 2 0 2
## 39 64 1 0 128 263 0 1 105 1 0.2 1 1 3
## 40 57 1 2 128 229 0 0 150 0 0.4 1 1 3
## 41 65 0 2 160 360 0 0 151 0 0.8 2 0 2
## 42 54 1 2 120 258 0 0 147 0 0.4 1 0 3
## 43 61 0 0 130 330 0 0 169 0 0.0 2 0 2
## 44 46 1 0 120 249 0 0 144 0 0.8 2 0 3
## 45 55 0 1 132 342 0 1 166 0 1.2 2 0 2
## 46 42 1 0 140 226 0 1 178 0 0.0 2 0 2
## 47 41 1 1 135 203 0 1 132 0 0.0 1 0 1
## 48 66 0 0 178 228 1 1 165 1 1.0 1 2 3
## 49 66 0 2 146 278 0 0 152 0 0.0 1 1 2
## 50 60 1 0 117 230 1 1 160 1 1.4 2 2 3
## 51 58 0 3 150 283 1 0 162 0 1.0 2 0 2
## 52 57 0 0 140 241 0 1 123 1 0.2 1 0 3
## 53 38 1 2 138 175 0 1 173 0 0.0 2 4 2
## 54 49 1 2 120 188 0 1 139 0 2.0 1 3 3
## 55 55 1 0 140 217 0 1 111 1 5.6 0 0 3
## 56 55 1 0 140 217 0 1 111 1 5.6 0 0 3
## 57 56 1 3 120 193 0 0 162 0 1.9 1 0 3
## 58 48 1 1 130 245 0 0 180 0 0.2 1 0 2
## 59 67 1 2 152 212 0 0 150 0 0.8 1 0 3
## 60 57 1 1 154 232 0 0 164 0 0.0 2 1 2
## 61 29 1 1 130 204 0 0 202 0 0.0 2 0 2
## 62 66 0 2 146 278 0 0 152 0 0.0 1 1 2
## 63 67 1 0 100 299 0 0 125 1 0.9 1 2 2
## 64 59 1 2 150 212 1 1 157 0 1.6 2 0 2
## 65 29 1 1 130 204 0 0 202 0 0.0 2 0 2
## 66 59 1 3 170 288 0 0 159 0 0.2 1 0 3
## 67 53 1 2 130 197 1 0 152 0 1.2 0 0 2
## 68 42 1 0 136 315 0 1 125 1 1.8 1 0 1
## 69 37 0 2 120 215 0 1 170 0 0.0 2 0 2
## 70 62 0 0 160 164 0 0 145 0 6.2 0 3 3
## 71 59 1 0 170 326 0 0 140 1 3.4 0 0 3
## 72 61 1 0 140 207 0 0 138 1 1.9 2 1 3
## 73 56 1 0 125 249 1 0 144 1 1.2 1 1 2
## 74 59 1 0 140 177 0 1 162 1 0.0 2 1 3
## 75 48 1 0 130 256 1 0 150 1 0.0 2 2 3
## 76 47 1 2 138 257 0 0 156 0 0.0 2 0 2
## 77 48 1 2 124 255 1 1 175 0 0.0 2 2 2
## 78 63 1 0 140 187 0 0 144 1 4.0 2 2 3
## 79 52 1 1 134 201 0 1 158 0 0.8 2 1 2
## 80 52 1 1 134 201 0 1 158 0 0.8 2 1 2
## 81 50 1 2 140 233 0 1 163 0 0.6 1 1 3
## 82 49 1 2 118 149 0 0 126 0 0.8 2 3 2
## 83 46 1 2 150 231 0 1 147 0 3.6 1 0 2
## 84 38 1 2 138 175 0 1 173 0 0.0 2 4 2
## 85 37 0 2 120 215 0 1 170 0 0.0 2 0 2
## 86 44 1 1 120 220 0 1 170 0 0.0 2 0 2
## 87 58 1 2 140 211 1 0 165 0 0.0 2 0 2
## 88 59 0 0 174 249 0 1 143 1 0.0 1 0 2
## 89 62 0 0 140 268 0 0 160 0 3.6 0 2 2
## 90 68 1 0 144 193 1 1 141 0 3.4 1 2 3
## 91 54 0 2 108 267 0 0 167 0 0.0 2 0 2
## 92 62 0 0 124 209 0 1 163 0 0.0 2 0 2
## 93 63 1 0 140 187 0 0 144 1 4.0 2 2 3
## 94 44 1 0 120 169 0 1 144 1 2.8 0 0 1
## 95 62 1 1 128 208 1 0 140 0 0.0 2 0 2
## 96 45 0 0 138 236 0 0 152 1 0.2 1 0 2
## 97 57 0 0 128 303 0 0 159 0 0.0 2 1 2
## 98 53 1 0 123 282 0 1 95 1 2.0 1 2 3
## 99 65 1 0 110 248 0 0 158 0 0.6 2 2 1
## 100 76 0 2 140 197 0 2 116 0 1.1 1 0 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
## 103 54 1 1 108 309 0 1 156 0 0.0 2 0 3
## 104 47 1 2 138 257 0 0 156 0 0.0 2 0 2
## 105 52 1 3 118 186 0 0 190 0 0.0 1 0 1
## 106 47 1 0 110 275 0 0 118 1 1.0 1 1 2
## 107 51 1 0 140 299 0 1 173 1 1.6 2 0 3
## 108 62 1 1 120 281 0 0 103 0 1.4 1 1 3
## 109 40 1 0 152 223 0 1 181 0 0.0 2 0 3
## 110 54 1 0 110 206 0 0 108 1 0.0 1 1 2
## 111 44 1 0 110 197 0 0 177 0 0.0 2 1 2
## 112 53 1 0 142 226 0 0 111 1 0.0 2 0 3
## 113 48 1 0 130 256 1 0 150 1 0.0 2 2 3
## 114 57 1 0 110 335 0 1 143 1 3.0 1 1 3
## 115 59 1 2 126 218 1 1 134 0 2.2 1 1 1
## 116 61 0 0 145 307 0 0 146 1 1.0 1 0 3
## 117 63 1 0 130 254 0 0 147 0 1.4 1 1 3
## 118 43 1 0 120 177 0 0 120 1 2.5 1 0 3
## 119 29 1 1 130 204 0 0 202 0 0.0 2 0 2
## 120 42 1 1 120 295 0 1 162 0 0.0 2 0 2
## 121 54 1 1 108 309 0 1 156 0 0.0 2 0 3
## 122 44 1 0 120 169 0 1 144 1 2.8 0 0 1
## 123 60 1 0 145 282 0 0 142 1 2.8 1 2 3
## 124 65 0 2 140 417 1 0 157 0 0.8 2 1 2
## 125 61 1 0 120 260 0 1 140 1 3.6 1 1 3
## 126 60 0 3 150 240 0 1 171 0 0.9 2 0 2
## 127 66 1 0 120 302 0 0 151 0 0.4 1 0 2
## 128 53 1 2 130 197 1 0 152 0 1.2 0 0 2
## 129 52 1 2 138 223 0 1 169 0 0.0 2 4 2
## 130 57 1 0 140 192 0 1 148 0 0.4 1 0 1
## 131 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
## 134 50 1 2 129 196 0 1 163 0 0.0 2 0 2
## 135 54 1 1 108 309 0 1 156 0 0.0 2 0 3
## 136 58 0 0 170 225 1 0 146 1 2.8 1 2 1
## 137 55 0 1 132 342 0 1 166 0 1.2 2 0 2
## 138 64 0 0 180 325 0 1 154 1 0.0 2 0 2
## 139 47 1 2 138 257 0 0 156 0 0.0 2 0 2
## 140 41 1 1 110 235 0 1 153 0 0.0 2 0 2
## 141 57 1 0 152 274 0 1 88 1 1.2 1 1 3
## 142 63 0 0 124 197 0 1 136 1 0.0 1 0 2
## 143 61 1 3 134 234 0 1 145 0 2.6 1 2 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
## 146 40 1 0 110 167 0 0 114 1 2.0 1 0 3
## 147 51 0 2 120 295 0 0 157 0 0.6 2 0 2
## 148 41 1 0 110 172 0 0 158 0 0.0 2 0 3
## 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
## 151 58 1 0 114 318 0 2 140 0 4.4 0 3 1
## 152 54 1 1 192 283 0 0 195 0 0.0 2 1 3
## 153 58 1 0 125 300 0 0 171 0 0.0 2 2 3
## 154 54 1 2 120 258 0 0 147 0 0.4 1 0 3
## 155 63 1 0 130 330 1 0 132 1 1.8 2 3 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
## 158 54 1 2 120 258 0 0 147 0 0.4 1 0 3
## 159 67 0 2 115 564 0 0 160 0 1.6 1 0 3
## 160 41 1 1 120 157 0 1 182 0 0.0 2 0 2
## 161 77 1 0 125 304 0 0 162 1 0.0 2 3 2
## 162 51 1 2 100 222 0 1 143 1 1.2 1 0 2
## 163 77 1 0 125 304 0 0 162 1 0.0 2 3 2
## 164 48 1 0 124 274 0 0 166 0 0.5 1 0 3
## 165 56 1 0 125 249 1 0 144 1 1.2 1 1 2
## 166 59 1 0 170 326 0 0 140 1 3.4 0 0 3
## 167 56 1 0 132 184 0 0 105 1 2.1 1 1 1
## 168 57 0 0 120 354 0 1 163 1 0.6 2 0 2
## 169 43 1 2 130 315 0 1 162 0 1.9 2 1 2
## 170 45 0 1 112 160 0 1 138 0 0.0 1 0 2
## 171 43 1 0 150 247 0 1 171 0 1.5 2 0 2
## 172 56 1 0 130 283 1 0 103 1 1.6 0 0 3
## 173 56 1 1 120 240 0 1 169 0 0.0 0 0 2
## 174 39 0 2 94 199 0 1 179 0 0.0 2 0 2
## 175 54 1 0 110 239 0 1 126 1 2.8 1 1 3
## 176 56 0 0 200 288 1 0 133 1 4.0 0 2 3
## 177 56 1 0 130 283 1 0 103 1 1.6 0 0 3
## 178 64 1 0 120 246 0 0 96 1 2.2 0 1 2
## 179 44 1 0 110 197 0 0 177 0 0.0 2 1 2
## 180 56 0 0 134 409 0 0 150 1 1.9 1 2 3
## 181 63 1 0 140 187 0 0 144 1 4.0 2 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
## 184 42 1 2 130 180 0 1 150 0 0.0 2 0 2
## 185 45 1 1 128 308 0 0 170 0 0.0 2 0 2
## 186 57 1 0 165 289 1 0 124 0 1.0 1 3 3
## 187 40 1 0 110 167 0 0 114 1 2.0 1 0 3
## 188 56 1 0 125 249 1 0 144 1 1.2 1 1 2
## 189 63 1 0 130 254 0 0 147 0 1.4 1 1 3
## 190 64 1 2 125 309 0 1 131 1 1.8 1 0 3
## 191 41 1 2 112 250 0 1 179 0 0.0 2 0 2
## 192 56 1 1 130 221 0 0 163 0 0.0 2 0 3
## 193 67 0 2 115 564 0 0 160 0 1.6 1 0 3
## 194 69 1 3 160 234 1 0 131 0 0.1 1 1 2
## 195 67 1 0 160 286 0 0 108 1 1.5 1 3 2
## 196 59 1 2 150 212 1 1 157 0 1.6 2 0 2
## 197 58 1 0 100 234 0 1 156 0 0.1 2 1 3
## 198 45 1 0 115 260 0 0 185 0 0.0 2 0 2
## 199 60 0 2 102 318 0 1 160 0 0.0 2 1 2
## 200 50 1 0 144 200 0 0 126 1 0.9 1 0 3
## 201 62 0 0 124 209 0 1 163 0 0.0 2 0 2
## 202 34 1 3 118 182 0 0 174 0 0.0 2 0 2
## 203 52 1 3 152 298 1 1 178 0 1.2 1 0 3
## 204 64 1 3 170 227 0 0 155 0 0.6 1 0 3
## 205 66 0 2 146 278 0 0 152 0 0.0 1 1 2
## 206 42 1 3 148 244 0 0 178 0 0.8 2 2 2
## 207 59 1 2 126 218 1 1 134 0 2.2 1 1 1
## 208 41 1 2 112 250 0 1 179 0 0.0 2 0 2
## 209 38 1 2 138 175 0 1 173 0 0.0 2 4 2
## 210 62 1 1 120 281 0 0 103 0 1.4 1 1 3
## 211 42 1 2 120 240 1 1 194 0 0.8 0 0 3
## 212 67 1 0 100 299 0 0 125 1 0.9 1 2 2
## 213 50 1 0 150 243 0 0 128 0 2.6 1 0 3
## 214 43 1 2 130 315 0 1 162 0 1.9 2 1 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
## 217 65 1 0 135 254 0 0 127 0 2.8 1 1 3
## 218 41 1 1 120 157 0 1 182 0 0.0 2 0 2
## 219 46 1 0 140 311 0 1 120 1 1.8 1 2 3
## 220 54 1 0 122 286 0 0 116 1 3.2 1 2 2
## 221 57 0 1 130 236 0 0 174 0 0.0 1 1 2
## 222 63 1 0 130 254 0 0 147 0 1.4 1 1 3
## 223 64 1 3 110 211 0 0 144 1 1.8 1 0 2
## 224 39 0 2 94 199 0 1 179 0 0.0 2 0 2
## 225 51 1 0 140 261 0 0 186 1 0.0 2 0 2
## 226 54 1 2 150 232 0 0 165 0 1.6 2 0 3
## 227 49 1 2 118 149 0 0 126 0 0.8 2 3 2
## 228 44 0 2 118 242 0 1 149 0 0.3 1 1 2
## 229 52 1 1 128 205 1 1 184 0 0.0 2 0 2
## 230 66 0 0 178 228 1 1 165 1 1.0 1 2 3
## 231 58 1 0 125 300 0 0 171 0 0.0 2 2 3
## 232 56 1 1 120 236 0 1 178 0 0.8 2 0 2
## 233 60 1 0 125 258 0 0 141 1 2.8 1 1 3
## 234 41 0 1 126 306 0 1 163 0 0.0 2 0 2
## 235 49 0 0 130 269 0 1 163 0 0.0 2 0 2
## 236 64 1 3 170 227 0 0 155 0 0.6 1 0 3
## 237 49 1 2 118 149 0 0 126 0 0.8 2 3 2
## 238 57 1 1 124 261 0 1 141 0 0.3 2 0 3
## 239 60 1 0 117 230 1 1 160 1 1.4 2 2 3
## 240 62 0 0 150 244 0 1 154 1 1.4 1 0 2
## 241 54 0 1 132 288 1 0 159 1 0.0 2 1 2
## 242 67 1 2 152 212 0 0 150 0 0.8 1 0 3
## 243 38 1 2 138 175 0 1 173 0 0.0 2 4 2
## 244 60 1 2 140 185 0 0 155 0 3.0 1 0 2
## 245 51 1 2 125 245 1 0 166 0 2.4 1 0 2
## 246 44 1 1 130 219 0 0 188 0 0.0 2 0 2
## 247 54 1 1 192 283 0 0 195 0 0.0 2 1 3
## 248 46 1 0 140 311 0 1 120 1 1.8 1 2 3
## 249 39 0 2 138 220 0 1 152 0 0.0 1 0 2
## 250 42 1 2 130 180 0 1 150 0 0.0 2 0 2
## 251 47 1 0 110 275 0 0 118 1 1.0 1 1 2
## 252 45 0 1 112 160 0 1 138 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
## 255 35 1 0 120 198 0 1 130 1 1.6 1 0 3
## 256 62 0 0 140 394 0 0 157 0 1.2 1 0 2
## 257 35 0 0 138 183 0 1 182 0 1.4 2 0 2
## 258 64 0 0 180 325 0 1 154 1 0.0 2 0 2
## 259 38 1 3 120 231 0 1 182 1 3.8 1 0 3
## 260 66 1 0 120 302 0 0 151 0 0.4 1 0 2
## 261 44 1 2 120 226 0 1 169 0 0.0 2 0 2
## 262 54 1 2 150 232 0 0 165 0 1.6 2 0 3
## 263 48 1 0 122 222 0 0 186 0 0.0 2 0 2
## 264 55 0 1 132 342 0 1 166 0 1.2 2 0 2
## 265 58 0 0 170 225 1 0 146 1 2.8 1 2 1
## 266 45 1 0 104 208 0 0 148 1 3.0 1 0 2
## 267 53 1 0 123 282 0 1 95 1 2.0 1 2 3
## 268 67 1 0 120 237 0 1 71 0 1.0 1 0 2
## 269 58 1 2 132 224 0 0 173 0 3.2 2 2 3
## 270 71 0 2 110 265 1 0 130 0 0.0 2 1 2
## 271 43 1 0 110 211 0 1 161 0 0.0 2 0 3
## 272 44 1 1 120 263 0 1 173 0 0.0 2 0 3
## 273 39 0 2 138 220 0 1 152 0 0.0 1 0 2
## 274 54 1 0 110 206 0 0 108 1 0.0 1 1 2
## 275 66 1 0 160 228 0 0 138 0 2.3 2 0 1
## 276 56 1 0 130 283 1 0 103 1 1.6 0 0 3
## 277 57 1 0 132 207 0 1 168 1 0.0 2 0 3
## 278 44 1 1 130 219 0 0 188 0 0.0 2 0 2
## 279 55 1 0 160 289 0 0 145 1 0.8 1 1 3
## 280 41 0 1 105 198 0 1 168 0 0.0 2 1 2
## 281 45 0 1 130 234 0 0 175 0 0.6 1 0 2
## 282 35 1 1 122 192 0 1 174 0 0.0 2 0 2
## 283 41 0 1 130 204 0 0 172 0 1.4 2 0 2
## 284 64 1 3 110 211 0 0 144 1 1.8 1 0 2
## 285 58 1 2 132 224 0 0 173 0 3.2 2 2 3
## 286 71 0 2 110 265 1 0 130 0 0.0 2 1 2
## 287 64 0 2 140 313 0 1 133 0 0.2 2 0 3
## 288 71 0 1 160 302 0 1 162 0 0.4 2 2 2
## 289 58 0 2 120 340 0 1 172 0 0.0 2 0 2
## 290 40 1 0 152 223 0 1 181 0 0.0 2 0 3
## 291 52 1 2 138 223 0 1 169 0 0.0 2 4 2
## 292 58 1 0 128 259 0 0 130 1 3.0 1 2 3
## 293 61 1 2 150 243 1 1 137 1 1.0 1 0 2
## 294 59 1 2 150 212 1 1 157 0 1.6 2 0 2
## 295 56 0 0 200 288 1 0 133 1 4.0 0 2 3
## 296 67 1 0 100 299 0 0 125 1 0.9 1 2 2
## 297 67 1 0 120 237 0 1 71 0 1.0 1 0 2
## 298 58 1 0 150 270 0 0 111 1 0.8 2 0 3
## 299 35 1 1 122 192 0 1 174 0 0.0 2 0 2
## 300 52 1 1 120 325 0 1 172 0 0.2 2 0 2
## 301 46 0 1 105 204 0 1 172 0 0.0 2 0 2
## 302 51 1 2 94 227 0 1 154 1 0.0 2 1 3
## 303 55 0 1 132 342 0 1 166 0 1.2 2 0 2
## 304 60 1 0 145 282 0 0 142 1 2.8 1 2 3
## 305 52 0 2 136 196 0 0 169 0 0.1 1 0 2
## 306 62 1 0 120 267 0 1 99 1 1.8 1 2 3
## 307 44 0 2 118 242 0 1 149 0 0.3 1 1 2
## 308 44 1 1 120 220 0 1 170 0 0.0 2 0 2
## 309 59 1 2 126 218 1 1 134 0 2.2 1 1 1
## 310 56 0 1 140 294 0 0 153 0 1.3 1 0 2
## 311 61 1 0 120 260 0 1 140 1 3.6 1 1 3
## 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
## 315 40 1 3 140 199 0 1 178 1 1.4 2 0 3
## 316 42 1 3 148 244 0 0 178 0 0.8 2 2 2
## 317 64 0 2 140 313 0 1 133 0 0.2 2 0 3
## 318 63 0 2 135 252 0 0 172 0 0.0 2 0 2
## 319 59 1 0 140 177 0 1 162 1 0.0 2 1 3
## 320 53 0 2 128 216 0 0 115 0 0.0 2 0 0
## 321 53 0 0 130 264 0 0 143 0 0.4 1 0 2
## 322 48 0 2 130 275 0 1 139 0 0.2 2 0 2
## 323 45 1 0 142 309 0 0 147 1 0.0 1 3 3
## 324 66 1 1 160 246 0 1 120 1 0.0 1 3 1
## 325 48 1 1 130 245 0 0 180 0 0.2 1 0 2
## 326 56 0 1 140 294 0 0 153 0 1.3 1 0 2
## 327 54 1 1 192 283 0 0 195 0 0.0 2 1 3
## 328 57 1 0 150 276 0 0 112 1 0.6 1 1 1
## 329 70 1 0 130 322 0 0 109 0 2.4 1 3 2
## 330 53 0 2 128 216 0 0 115 0 0.0 2 0 0
## 331 37 0 2 120 215 0 1 170 0 0.0 2 0 2
## 332 63 0 0 108 269 0 1 169 1 1.8 1 2 2
## 333 37 1 2 130 250 0 1 187 0 3.5 0 0 2
## 334 54 0 2 110 214 0 1 158 0 1.6 1 0 2
## 335 60 1 0 130 206 0 0 132 1 2.4 1 2 3
## 336 58 1 0 150 270 0 0 111 1 0.8 2 0 3
## 337 57 1 2 150 126 1 1 173 0 0.2 2 1 3
## 338 54 1 2 125 273 0 0 152 0 0.5 0 1 2
## 339 56 1 2 130 256 1 0 142 1 0.6 1 1 1
## 340 60 1 0 130 253 0 1 144 1 1.4 2 1 3
## 341 38 1 2 138 175 0 1 173 0 0.0 2 4 2
## 342 44 1 2 120 226 0 1 169 0 0.0 2 0 2
## 343 65 0 2 155 269 0 1 148 0 0.8 2 0 2
## 344 52 1 2 172 199 1 1 162 0 0.5 2 0 3
## 345 41 1 1 120 157 0 1 182 0 0.0 2 0 2
## 346 66 1 1 160 246 0 1 120 1 0.0 1 3 1
## 347 50 1 0 150 243 0 0 128 0 2.6 1 0 3
## 348 54 0 2 108 267 0 0 167 0 0.0 2 0 2
## 349 43 1 0 132 247 1 0 143 1 0.1 1 4 3
## 350 62 0 2 130 263 0 1 97 0 1.2 1 1 3
## 351 66 1 0 120 302 0 0 151 0 0.4 1 0 2
## 352 50 1 0 144 200 0 0 126 1 0.9 1 0 3
## 353 57 1 0 110 335 0 1 143 1 3.0 1 1 3
## 354 57 1 0 110 201 0 1 126 1 1.5 1 0 1
## 355 57 1 1 124 261 0 1 141 0 0.3 2 0 3
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## 1025 0Partir los datos 80-20
set.seed(123)
renglones_entremiento <- createDataPartition(df$target, p=0.8, list=FALSE)
entrenamiento <- df[renglones_entremiento, ]
prueba <- df[-renglones_entremiento, ]Metodos para Modelar
Los metodos mas utilizados para modelar aprendizaje automatico son:
- SVM: Support Vector Machine o Maquina de
Vectores de Soporte. Hay varios subtipos: Lineal (svmLinear), Radial
(svmRadial), Polinomico (svmPoly), etc.
- Arbol de Decision: rpart
- Redes Neuronales: nnet
- Random Forest: o Bosques Aleatorios: rf
1. Modelo con el Metodo svmLinear
modelo1 <- train(target ~. , data=entrenamiento,
method = "svmLinear", #Cambiar,
preProcess= c("scale", "center"), #Existe el pre-procesamiento pero asi esta bn
trControl = trainControl(method="cv", number=10), #Cross Validation SIEMPRE
tuneGrid = data.frame(C=1) #Cambiar
)
resultado_entrenamiento1 <- predict(modelo1, entrenamiento)
resultado_prueba1 <- predict(modelo1, prueba)
# Matriz de Confusion del Resultado del Entrenamiento1
mcre1 <- confusionMatrix(resultado_entrenamiento1, entrenamiento$target)
# Matriz de Confusion del Resultado de la Prueba1
mcrp1 <- confusionMatrix(resultado_prueba1, prueba$target)2. Modelo con el Metodo svmRadial
modelo2 <- train(target ~. , data=entrenamiento,
method = "svmRadial", #Cambiar,
preProcess= c("scale", "center"), #Existe el pre-procesamiento pero asi esta bn
trControl = trainControl(method="cv", number=10), #Cross Validation SIEMPRE
tuneGrid = data.frame(sigma=1, C=1) #Cambiar
)
resultado_entrenamiento2 <- predict(modelo2, entrenamiento)
resultado_prueba2 <- predict(modelo2, prueba)
# Matriz de Confusion del Resultado del Entrenamiento1
mcre2 <- confusionMatrix(resultado_entrenamiento2, entrenamiento$target)
# Matriz de Confusion del Resultado de la Prueba1
mcrp2 <- confusionMatrix(resultado_prueba2, prueba$target)3. Modelo con el Metodo svmPoly
modelo3 <- train(target ~. , data=entrenamiento,
method = "svmPoly", #Cambiar,
preProcess= c("scale", "center"), #Existe el pre-procesamiento pero asi esta bn
trControl = trainControl(method="cv", number=10), #Cross Validation SIEMPRE
tuneGrid = data.frame(degree=1, scale=1, C=1) #Cambiar
)
resultado_entrenamiento3 <- predict(modelo3, entrenamiento)
resultado_prueba3 <- predict(modelo3, prueba)
# Matriz de Confusion del Resultado del Entrenamiento1
mcre3 <- confusionMatrix(resultado_entrenamiento3, entrenamiento$target)
# Matriz de Confusion del Resultado de la Prueba1
mcrp3 <- confusionMatrix(resultado_prueba3, prueba$target)4. Modelo con el Metodo rpart
modelo4 <- train(target ~. , data=entrenamiento,
method = "rpart", #Cambiar,
preProcess= c("scale", "center"), #Existe el pre-procesamiento pero asi esta bn
trControl = trainControl(method="cv", number=10), #Cross Validation SIEMPRE
tuneLength = 10 #Cambiar
)
resultado_entrenamiento4 <- predict(modelo4, entrenamiento)
resultado_prueba4 <- predict(modelo4, prueba)
# Matriz de Confusion del Resultado del Entrenamiento1
mcre4 <- confusionMatrix(resultado_entrenamiento4, entrenamiento$target)
# Matriz de Confusion del Resultado de la Prueba1
mcrp4 <- confusionMatrix(resultado_prueba4, prueba$target)5. Modelo con el Metodo nnet
modelo5 <- train(target ~. , data=entrenamiento,
method = "nnet", #Cambiar,
preProcess= c("scale", "center"), #Existe el pre-procesamiento pero asi esta bn
trControl = trainControl(method="cv", number=10) #Cross Validation SIEMPRE
#Cambiar
)## # weights: 25
## initial value 534.715512
## iter 10 value 223.363750
## iter 20 value 209.869954
## iter 30 value 198.570677
## iter 40 value 177.540547
## iter 50 value 177.395285
## final value 177.395138
## converged
## # weights: 73
## initial value 572.745922
## iter 10 value 194.246195
## iter 20 value 152.191711
## iter 30 value 126.614638
## iter 40 value 92.543694
## iter 50 value 78.746555
## iter 60 value 67.755667
## iter 70 value 66.078737
## iter 80 value 64.921139
## iter 90 value 64.707146
## iter 100 value 64.596336
## final value 64.596336
## stopped after 100 iterations
## # weights: 121
## initial value 552.772751
## iter 10 value 190.415083
## iter 20 value 118.504550
## iter 30 value 91.508326
## iter 40 value 85.984039
## iter 50 value 82.755252
## iter 60 value 82.213884
## iter 70 value 81.589103
## iter 80 value 81.273209
## iter 90 value 81.108966
## iter 100 value 80.984541
## final value 80.984541
## stopped after 100 iterations
## # weights: 25
## initial value 532.758179
## iter 10 value 298.830332
## iter 20 value 250.160990
## iter 30 value 222.766098
## iter 40 value 213.792124
## iter 50 value 211.150852
## iter 60 value 210.967881
## iter 70 value 210.947310
## final value 210.941946
## converged
## # weights: 73
## initial value 616.455260
## iter 10 value 220.367230
## iter 20 value 178.688409
## iter 30 value 161.690178
## iter 40 value 154.018812
## iter 50 value 149.620010
## iter 60 value 147.906537
## iter 70 value 146.746374
## iter 80 value 142.285114
## iter 90 value 140.188410
## iter 100 value 140.023100
## final value 140.023100
## stopped after 100 iterations
## # weights: 121
## initial value 545.022132
## iter 10 value 211.260881
## iter 20 value 170.581921
## iter 30 value 139.648122
## iter 40 value 117.500230
## iter 50 value 109.681950
## iter 60 value 107.651362
## iter 70 value 102.899352
## iter 80 value 96.582371
## iter 90 value 94.576403
## iter 100 value 92.591197
## final value 92.591197
## stopped after 100 iterations
## # weights: 25
## initial value 537.456314
## iter 10 value 240.443521
## iter 20 value 222.687610
## iter 30 value 214.577901
## iter 40 value 183.448119
## iter 50 value 175.384834
## iter 60 value 173.731786
## iter 70 value 172.667693
## iter 80 value 172.619775
## iter 90 value 172.461230
## iter 100 value 172.343854
## final value 172.343854
## stopped after 100 iterations
## # weights: 73
## initial value 515.560933
## iter 10 value 204.949223
## iter 20 value 159.397184
## iter 30 value 147.088050
## iter 40 value 139.616109
## iter 50 value 136.316037
## iter 60 value 130.467866
## iter 70 value 128.043107
## iter 80 value 127.993430
## iter 90 value 127.953418
## iter 100 value 127.560573
## final value 127.560573
## stopped after 100 iterations
## # weights: 121
## initial value 485.119713
## iter 10 value 176.720126
## iter 20 value 113.262373
## iter 30 value 79.493830
## iter 40 value 63.600350
## iter 50 value 57.875444
## iter 60 value 55.596528
## iter 70 value 55.118518
## iter 80 value 54.482878
## iter 90 value 53.590291
## iter 100 value 52.904660
## final value 52.904660
## stopped after 100 iterations
## # weights: 25
## initial value 524.413808
## iter 10 value 295.569975
## iter 20 value 217.571715
## iter 30 value 194.948517
## iter 40 value 187.872114
## iter 50 value 181.724110
## iter 60 value 174.102684
## iter 70 value 173.776396
## iter 80 value 171.829884
## iter 90 value 170.111166
## iter 100 value 169.517958
## final value 169.517958
## stopped after 100 iterations
## # weights: 73
## initial value 524.120998
## iter 10 value 212.471264
## iter 20 value 172.690059
## iter 30 value 140.941742
## iter 40 value 126.279258
## iter 50 value 113.987877
## iter 60 value 110.974564
## iter 70 value 109.794101
## iter 80 value 108.107517
## iter 90 value 107.380018
## iter 100 value 106.518703
## final value 106.518703
## stopped after 100 iterations
## # weights: 121
## initial value 547.059822
## iter 10 value 221.311315
## iter 20 value 166.044853
## iter 30 value 129.461313
## iter 40 value 114.292234
## iter 50 value 111.550705
## iter 60 value 108.587919
## iter 70 value 107.733316
## iter 80 value 107.558550
## iter 90 value 107.313274
## iter 100 value 107.278863
## final value 107.278863
## stopped after 100 iterations
## # weights: 25
## initial value 540.296279
## iter 10 value 297.914924
## iter 20 value 236.444748
## iter 30 value 207.296704
## iter 40 value 203.111193
## iter 50 value 203.077087
## final value 203.076976
## converged
## # weights: 73
## initial value 536.723212
## iter 10 value 206.418161
## iter 20 value 175.663989
## iter 30 value 153.396351
## iter 40 value 146.907331
## iter 50 value 136.384178
## iter 60 value 132.605976
## iter 70 value 131.960333
## iter 80 value 131.874042
## iter 90 value 131.872685
## final value 131.872539
## converged
## # weights: 121
## initial value 551.272696
## iter 10 value 189.097449
## iter 20 value 147.268490
## iter 30 value 122.671410
## iter 40 value 107.268407
## iter 50 value 100.484561
## iter 60 value 92.649410
## iter 70 value 83.206523
## iter 80 value 76.720238
## iter 90 value 74.042871
## iter 100 value 72.866357
## final value 72.866357
## stopped after 100 iterations
## # weights: 25
## initial value 515.890725
## iter 10 value 296.723613
## iter 20 value 223.065052
## iter 30 value 215.950306
## iter 40 value 210.458340
## iter 50 value 205.704425
## iter 60 value 194.387688
## iter 70 value 193.372333
## iter 80 value 191.829666
## iter 90 value 190.579366
## iter 100 value 190.568937
## final value 190.568937
## stopped after 100 iterations
## # weights: 73
## initial value 563.534438
## iter 10 value 246.941180
## iter 20 value 183.618271
## iter 30 value 141.671120
## iter 40 value 120.346910
## iter 50 value 115.625041
## iter 60 value 112.064574
## iter 70 value 107.948783
## iter 80 value 107.686302
## iter 90 value 107.155231
## iter 100 value 106.341384
## final value 106.341384
## stopped after 100 iterations
## # weights: 121
## initial value 513.305500
## iter 10 value 150.795079
## iter 20 value 88.198039
## iter 30 value 62.370165
## iter 40 value 52.342423
## iter 50 value 49.453440
## iter 60 value 46.372881
## iter 70 value 44.955872
## iter 80 value 42.525007
## iter 90 value 41.403307
## iter 100 value 41.199317
## final value 41.199317
## stopped after 100 iterations
## # weights: 25
## initial value 530.576334
## iter 10 value 273.812296
## iter 20 value 230.362649
## iter 30 value 213.487875
## iter 40 value 190.047267
## iter 50 value 184.808654
## iter 60 value 184.797714
## final value 184.797642
## converged
## # weights: 73
## initial value 524.549909
## iter 10 value 224.223441
## iter 20 value 171.688857
## iter 30 value 145.659287
## iter 40 value 133.859118
## iter 50 value 131.235786
## iter 60 value 129.271180
## iter 70 value 128.287737
## iter 80 value 127.968055
## iter 90 value 127.651321
## iter 100 value 127.131750
## final value 127.131750
## stopped after 100 iterations
## # weights: 121
## initial value 561.614928
## iter 10 value 168.924128
## iter 20 value 121.510553
## iter 30 value 93.639358
## iter 40 value 84.681283
## iter 50 value 78.413660
## iter 60 value 76.552322
## iter 70 value 75.446137
## iter 80 value 75.248354
## iter 90 value 75.063710
## iter 100 value 75.031249
## final value 75.031249
## stopped after 100 iterations
## # weights: 25
## initial value 512.561986
## iter 10 value 302.881228
## iter 20 value 260.945723
## iter 30 value 216.968473
## iter 40 value 206.127158
## iter 50 value 200.709312
## iter 60 value 200.608473
## iter 70 value 200.544339
## final value 200.537956
## converged
## # weights: 73
## initial value 545.405531
## iter 10 value 215.790810
## iter 20 value 185.017753
## iter 30 value 158.774358
## iter 40 value 144.323270
## iter 50 value 135.396610
## iter 60 value 132.940858
## iter 70 value 131.698254
## iter 80 value 131.264332
## iter 90 value 131.163203
## iter 100 value 131.084761
## final value 131.084761
## stopped after 100 iterations
## # weights: 121
## initial value 671.606874
## iter 10 value 182.491812
## iter 20 value 149.613896
## iter 30 value 138.626112
## iter 40 value 125.912307
## iter 50 value 116.779793
## iter 60 value 114.292329
## iter 70 value 99.436411
## iter 80 value 94.826645
## iter 90 value 90.191510
## iter 100 value 88.029433
## final value 88.029433
## stopped after 100 iterations
## # weights: 25
## initial value 528.043339
## iter 10 value 244.870616
## iter 20 value 201.635439
## iter 30 value 188.794163
## iter 40 value 173.448760
## iter 50 value 172.059521
## iter 60 value 171.916033
## iter 70 value 171.625049
## iter 80 value 171.576975
## iter 90 value 171.545543
## iter 100 value 171.493613
## final value 171.493613
## stopped after 100 iterations
## # weights: 73
## initial value 657.506520
## iter 10 value 190.289177
## iter 20 value 144.672084
## iter 30 value 129.545717
## iter 40 value 110.468785
## iter 50 value 104.403905
## iter 60 value 102.945795
## iter 70 value 102.473290
## iter 80 value 102.108009
## iter 90 value 101.902328
## iter 100 value 100.896297
## final value 100.896297
## stopped after 100 iterations
## # weights: 121
## initial value 526.225241
## iter 10 value 208.370011
## iter 20 value 123.114124
## iter 30 value 76.897954
## iter 40 value 58.387977
## iter 50 value 47.700504
## iter 60 value 44.996865
## iter 70 value 44.251733
## iter 80 value 43.823835
## iter 90 value 35.444655
## iter 100 value 31.988866
## final value 31.988866
## stopped after 100 iterations
## # weights: 25
## initial value 496.906661
## iter 10 value 227.927278
## iter 20 value 204.826169
## iter 30 value 195.942716
## iter 40 value 170.735448
## iter 50 value 159.636670
## iter 60 value 159.571738
## iter 70 value 159.564456
## iter 80 value 159.561491
## iter 90 value 159.561266
## iter 100 value 159.561047
## final value 159.561047
## stopped after 100 iterations
## # weights: 73
## initial value 534.209669
## iter 10 value 199.713573
## iter 20 value 151.862104
## iter 30 value 130.706034
## iter 40 value 121.202323
## iter 50 value 114.706192
## iter 60 value 111.864043
## iter 70 value 107.815705
## iter 80 value 106.800457
## iter 90 value 106.704295
## final value 106.703846
## converged
## # weights: 121
## initial value 513.609801
## iter 10 value 127.826039
## iter 20 value 62.397927
## iter 30 value 48.923657
## iter 40 value 44.048908
## iter 50 value 42.678562
## iter 60 value 41.025312
## iter 70 value 39.196341
## iter 80 value 38.718559
## iter 90 value 38.448806
## iter 100 value 37.497415
## final value 37.497415
## stopped after 100 iterations
## # weights: 25
## initial value 518.151021
## iter 10 value 318.678096
## iter 20 value 241.945332
## iter 30 value 229.638357
## iter 40 value 215.367969
## iter 50 value 206.469115
## iter 60 value 206.251369
## final value 206.251364
## converged
## # weights: 73
## initial value 530.833849
## iter 10 value 338.128042
## iter 20 value 270.513062
## iter 30 value 221.510693
## iter 40 value 178.916613
## iter 50 value 158.169247
## iter 60 value 154.261671
## iter 70 value 153.263984
## iter 80 value 152.546474
## iter 90 value 151.021312
## iter 100 value 150.566527
## final value 150.566527
## stopped after 100 iterations
## # weights: 121
## initial value 565.658486
## iter 10 value 280.189051
## iter 20 value 196.660781
## iter 30 value 143.227586
## iter 40 value 117.647916
## iter 50 value 103.919585
## iter 60 value 94.168347
## iter 70 value 86.161121
## iter 80 value 81.207663
## iter 90 value 78.711033
## iter 100 value 77.422134
## final value 77.422134
## stopped after 100 iterations
## # weights: 25
## initial value 558.963055
## iter 10 value 299.302652
## iter 20 value 227.724495
## iter 30 value 214.949887
## iter 40 value 211.230070
## iter 50 value 211.113771
## iter 60 value 207.671485
## iter 70 value 207.470557
## iter 80 value 207.428745
## iter 90 value 207.424099
## iter 100 value 207.423016
## final value 207.423016
## stopped after 100 iterations
## # weights: 73
## initial value 555.065998
## iter 10 value 180.867594
## iter 20 value 127.554671
## iter 30 value 97.180965
## iter 40 value 87.305561
## iter 50 value 85.099076
## iter 60 value 84.642955
## iter 70 value 84.184183
## iter 80 value 83.930768
## iter 90 value 83.197667
## iter 100 value 82.844331
## final value 82.844331
## stopped after 100 iterations
## # weights: 121
## initial value 535.930796
## iter 10 value 172.540069
## iter 20 value 106.929591
## iter 30 value 71.405229
## iter 40 value 53.528710
## iter 50 value 49.097767
## iter 60 value 41.476905
## iter 70 value 39.775296
## iter 80 value 32.149589
## iter 90 value 24.982120
## iter 100 value 23.739887
## final value 23.739887
## stopped after 100 iterations
## # weights: 25
## initial value 514.080659
## iter 10 value 268.555888
## iter 20 value 227.994652
## iter 30 value 213.904593
## iter 40 value 205.291511
## iter 50 value 196.076685
## iter 60 value 192.252637
## iter 70 value 189.517954
## iter 80 value 185.095985
## iter 90 value 176.724053
## iter 100 value 176.683031
## final value 176.683031
## stopped after 100 iterations
## # weights: 73
## initial value 510.682499
## iter 10 value 179.887314
## iter 20 value 127.635941
## iter 30 value 106.375525
## iter 40 value 97.626447
## iter 50 value 89.293499
## iter 60 value 87.930155
## iter 70 value 87.918810
## final value 87.918765
## converged
## # weights: 121
## initial value 534.557153
## iter 10 value 192.055188
## iter 20 value 106.471874
## iter 30 value 68.157389
## iter 40 value 60.303054
## iter 50 value 57.888174
## iter 60 value 56.108330
## iter 70 value 52.631391
## iter 80 value 52.311097
## iter 90 value 51.221122
## iter 100 value 50.179478
## final value 50.179478
## stopped after 100 iterations
## # weights: 25
## initial value 586.762145
## iter 10 value 279.756970
## iter 20 value 238.839745
## iter 30 value 222.078970
## iter 40 value 219.375113
## iter 50 value 218.660685
## iter 60 value 218.505140
## iter 70 value 213.041871
## iter 80 value 212.591090
## iter 90 value 212.541483
## final value 212.541418
## converged
## # weights: 73
## initial value 537.824697
## iter 10 value 222.473142
## iter 20 value 195.134011
## iter 30 value 169.929303
## iter 40 value 151.462068
## iter 50 value 143.005184
## iter 60 value 142.089657
## iter 70 value 141.677336
## iter 80 value 141.550118
## iter 90 value 141.535187
## iter 100 value 141.534488
## final value 141.534488
## stopped after 100 iterations
## # weights: 121
## initial value 523.052725
## iter 10 value 234.813273
## iter 20 value 151.115173
## iter 30 value 116.206558
## iter 40 value 105.017422
## iter 50 value 97.891829
## iter 60 value 93.755609
## iter 70 value 91.023653
## iter 80 value 89.234505
## iter 90 value 88.982824
## iter 100 value 88.826263
## final value 88.826263
## stopped after 100 iterations
## # weights: 25
## initial value 577.005987
## iter 10 value 243.205547
## iter 20 value 210.027134
## iter 30 value 198.598464
## iter 40 value 176.594694
## iter 50 value 175.697600
## iter 60 value 175.370267
## iter 70 value 175.271829
## iter 80 value 175.258453
## iter 90 value 175.246010
## iter 100 value 175.228476
## final value 175.228476
## stopped after 100 iterations
## # weights: 73
## initial value 567.383546
## iter 10 value 217.054749
## iter 20 value 163.272972
## iter 30 value 148.189837
## iter 40 value 142.441783
## iter 50 value 140.360317
## iter 60 value 138.386061
## iter 70 value 137.306698
## iter 80 value 137.039418
## iter 90 value 131.652550
## iter 100 value 131.544671
## final value 131.544671
## stopped after 100 iterations
## # weights: 121
## initial value 580.015084
## iter 10 value 175.219350
## iter 20 value 83.818059
## iter 30 value 51.814087
## iter 40 value 44.824881
## iter 50 value 41.480529
## iter 60 value 40.473464
## iter 70 value 40.042688
## iter 80 value 39.753456
## iter 90 value 39.568942
## iter 100 value 39.255546
## final value 39.255546
## stopped after 100 iterations
## # weights: 25
## initial value 519.997176
## iter 10 value 337.293526
## iter 20 value 237.014623
## iter 30 value 215.088561
## iter 40 value 212.545772
## iter 50 value 204.735089
## iter 60 value 192.552718
## iter 70 value 175.678167
## iter 80 value 174.270310
## iter 90 value 161.104875
## iter 100 value 159.920089
## final value 159.920089
## stopped after 100 iterations
## # weights: 73
## initial value 532.030675
## iter 10 value 266.200230
## iter 20 value 179.194837
## iter 30 value 146.521497
## iter 40 value 136.482376
## iter 50 value 131.371840
## iter 60 value 126.793256
## iter 70 value 118.170122
## iter 80 value 116.165280
## iter 90 value 113.316185
## iter 100 value 110.244552
## final value 110.244552
## stopped after 100 iterations
## # weights: 121
## initial value 506.627907
## iter 10 value 192.330945
## iter 20 value 127.738074
## iter 30 value 86.905957
## iter 40 value 67.978807
## iter 50 value 64.471510
## iter 60 value 62.819121
## iter 70 value 62.368601
## iter 80 value 62.344584
## iter 90 value 62.317026
## iter 100 value 62.005964
## final value 62.005964
## stopped after 100 iterations
## # weights: 25
## initial value 531.998317
## iter 10 value 316.540442
## iter 20 value 259.026321
## iter 30 value 232.489630
## iter 40 value 209.374541
## iter 50 value 204.875161
## iter 60 value 204.688415
## iter 70 value 204.636980
## final value 204.634356
## converged
## # weights: 73
## initial value 513.857827
## iter 10 value 260.197927
## iter 20 value 206.205019
## iter 30 value 178.847820
## iter 40 value 156.926288
## iter 50 value 146.791836
## iter 60 value 142.252923
## iter 70 value 139.017873
## iter 80 value 137.699279
## iter 90 value 137.463740
## iter 100 value 137.444569
## final value 137.444569
## stopped after 100 iterations
## # weights: 121
## initial value 531.701345
## iter 10 value 203.279757
## iter 20 value 132.176512
## iter 30 value 99.361285
## iter 40 value 80.392178
## iter 50 value 73.665138
## iter 60 value 71.350142
## iter 70 value 69.437480
## iter 80 value 68.213945
## iter 90 value 67.502894
## iter 100 value 66.286726
## final value 66.286726
## stopped after 100 iterations
## # weights: 25
## initial value 520.025084
## iter 10 value 293.197464
## iter 20 value 238.167433
## iter 30 value 216.126975
## iter 40 value 202.923973
## iter 50 value 189.376344
## iter 60 value 188.782848
## iter 70 value 188.586691
## iter 80 value 188.550752
## iter 90 value 188.533234
## iter 100 value 188.511786
## final value 188.511786
## stopped after 100 iterations
## # weights: 73
## initial value 549.276565
## iter 10 value 202.424577
## iter 20 value 120.498977
## iter 30 value 100.218470
## iter 40 value 85.569796
## iter 50 value 82.218717
## iter 60 value 80.805077
## iter 70 value 76.670669
## iter 80 value 76.100468
## iter 90 value 75.950664
## iter 100 value 75.837156
## final value 75.837156
## stopped after 100 iterations
## # weights: 121
## initial value 486.801109
## iter 10 value 162.586173
## iter 20 value 92.472285
## iter 30 value 63.280457
## iter 40 value 58.375504
## iter 50 value 56.479074
## iter 60 value 56.166020
## iter 70 value 56.034755
## iter 80 value 49.839032
## iter 90 value 47.800762
## iter 100 value 47.738452
## final value 47.738452
## stopped after 100 iterations
## # weights: 25
## initial value 515.321427
## iter 10 value 374.746642
## iter 20 value 264.816557
## iter 30 value 217.620535
## iter 40 value 199.793830
## iter 50 value 172.772090
## iter 60 value 168.882395
## iter 70 value 168.783296
## iter 80 value 168.738056
## iter 90 value 168.722188
## iter 100 value 168.714845
## final value 168.714845
## stopped after 100 iterations
## # weights: 73
## initial value 572.217448
## iter 10 value 254.080902
## iter 20 value 186.652289
## iter 30 value 163.244201
## iter 40 value 141.023448
## iter 50 value 134.881444
## iter 60 value 132.591327
## iter 70 value 125.242198
## iter 80 value 125.082199
## iter 90 value 125.032124
## iter 100 value 125.004077
## final value 125.004077
## stopped after 100 iterations
## # weights: 121
## initial value 526.567787
## iter 10 value 177.428831
## iter 20 value 109.604681
## iter 30 value 80.034681
## iter 40 value 71.246729
## iter 50 value 67.295835
## iter 60 value 61.091073
## iter 70 value 57.646852
## iter 80 value 56.382133
## iter 90 value 55.321039
## iter 100 value 54.170818
## final value 54.170818
## stopped after 100 iterations
## # weights: 25
## initial value 510.269611
## iter 10 value 240.884378
## iter 20 value 221.021402
## iter 30 value 213.662528
## iter 40 value 212.891130
## final value 212.890763
## converged
## # weights: 73
## initial value 633.717883
## iter 10 value 335.115868
## iter 20 value 221.363186
## iter 30 value 186.382241
## iter 40 value 154.570270
## iter 50 value 141.029164
## iter 60 value 133.763365
## iter 70 value 130.569600
## iter 80 value 125.288191
## iter 90 value 124.408629
## iter 100 value 124.269476
## final value 124.269476
## stopped after 100 iterations
## # weights: 121
## initial value 519.420760
## iter 10 value 202.494150
## iter 20 value 153.513345
## iter 30 value 132.994726
## iter 40 value 125.035358
## iter 50 value 122.480473
## iter 60 value 121.654446
## iter 70 value 121.533779
## iter 80 value 121.415922
## iter 90 value 118.241332
## iter 100 value 108.877918
## final value 108.877918
## stopped after 100 iterations
## # weights: 25
## initial value 534.337762
## iter 10 value 304.298241
## iter 20 value 269.384794
## iter 30 value 247.819230
## iter 40 value 235.887302
## iter 50 value 223.871379
## iter 60 value 221.359784
## iter 70 value 218.882279
## iter 80 value 218.841898
## iter 90 value 218.835967
## iter 100 value 218.835073
## final value 218.835073
## stopped after 100 iterations
## # weights: 73
## initial value 545.502747
## iter 10 value 285.531002
## iter 20 value 222.690559
## iter 30 value 178.709869
## iter 40 value 169.416660
## iter 50 value 161.293922
## iter 60 value 154.744867
## iter 70 value 151.961957
## iter 80 value 151.707400
## iter 90 value 151.489054
## iter 100 value 148.683822
## final value 148.683822
## stopped after 100 iterations
## # weights: 121
## initial value 546.052860
## iter 10 value 208.491524
## iter 20 value 116.493820
## iter 30 value 60.958446
## iter 40 value 45.114528
## iter 50 value 39.890971
## iter 60 value 34.546262
## iter 70 value 31.457760
## iter 80 value 28.161345
## iter 90 value 27.063840
## iter 100 value 26.695138
## final value 26.695138
## stopped after 100 iterations
## # weights: 25
## initial value 514.188818
## iter 10 value 284.843848
## iter 20 value 241.746632
## iter 30 value 226.575339
## iter 40 value 216.855770
## iter 50 value 213.619677
## iter 60 value 208.142256
## iter 70 value 206.641634
## iter 80 value 200.500824
## iter 90 value 195.762697
## iter 100 value 195.634168
## final value 195.634168
## stopped after 100 iterations
## # weights: 73
## initial value 507.590751
## iter 10 value 204.139765
## iter 20 value 151.372138
## iter 30 value 128.860421
## iter 40 value 115.145026
## iter 50 value 110.394294
## iter 60 value 108.504177
## iter 70 value 99.298406
## iter 80 value 97.902353
## iter 90 value 97.071562
## iter 100 value 96.782485
## final value 96.782485
## stopped after 100 iterations
## # weights: 121
## initial value 642.257183
## iter 10 value 177.785884
## iter 20 value 113.151299
## iter 30 value 80.971671
## iter 40 value 72.881241
## iter 50 value 70.038134
## iter 60 value 69.593229
## iter 70 value 69.552396
## iter 80 value 69.550653
## iter 90 value 69.549159
## iter 100 value 69.548639
## final value 69.548639
## stopped after 100 iterations
## # weights: 25
## initial value 493.834895
## iter 10 value 296.809643
## iter 20 value 232.191490
## iter 30 value 213.748731
## iter 40 value 211.396039
## iter 50 value 211.133742
## iter 60 value 211.132134
## iter 70 value 211.132070
## final value 211.132045
## converged
## # weights: 73
## initial value 499.668010
## iter 10 value 195.434887
## iter 20 value 177.953076
## iter 30 value 169.915107
## iter 40 value 165.354536
## iter 50 value 163.766019
## iter 60 value 163.196369
## iter 70 value 163.147981
## iter 80 value 163.143525
## final value 163.143362
## converged
## # weights: 121
## initial value 659.457381
## iter 10 value 221.448783
## iter 20 value 160.962283
## iter 30 value 130.930886
## iter 40 value 110.781236
## iter 50 value 105.351200
## iter 60 value 102.198898
## iter 70 value 99.486816
## iter 80 value 92.378229
## iter 90 value 83.236206
## iter 100 value 81.571272
## final value 81.571272
## stopped after 100 iterations
## # weights: 25
## initial value 541.077503
## iter 10 value 274.109078
## iter 20 value 241.264534
## iter 30 value 215.108242
## iter 40 value 203.156383
## iter 50 value 183.714258
## iter 60 value 183.349988
## iter 70 value 183.230116
## iter 80 value 183.183624
## iter 90 value 183.143481
## iter 100 value 183.050446
## final value 183.050446
## stopped after 100 iterations
## # weights: 73
## initial value 523.529199
## iter 10 value 207.785244
## iter 20 value 152.316322
## iter 30 value 127.302669
## iter 40 value 123.506949
## iter 50 value 120.328963
## iter 60 value 112.974902
## iter 70 value 109.949303
## iter 80 value 106.229783
## iter 90 value 104.356212
## iter 100 value 103.827350
## final value 103.827350
## stopped after 100 iterations
## # weights: 121
## initial value 482.884400
## iter 10 value 174.096457
## iter 20 value 119.073191
## iter 30 value 81.497925
## iter 40 value 76.493081
## iter 50 value 75.006680
## iter 60 value 74.324558
## iter 70 value 72.964427
## iter 80 value 72.187138
## iter 90 value 71.731750
## iter 100 value 71.594790
## final value 71.594790
## stopped after 100 iterations
## # weights: 25
## initial value 536.081896
## iter 10 value 276.490457
## iter 20 value 203.553763
## iter 30 value 186.245891
## iter 40 value 173.240115
## iter 50 value 168.305256
## iter 60 value 168.268019
## final value 168.267957
## converged
## # weights: 73
## initial value 534.842360
## iter 10 value 204.487422
## iter 20 value 163.482965
## iter 30 value 142.077628
## iter 40 value 130.803010
## iter 50 value 120.021725
## iter 60 value 111.420325
## iter 70 value 111.207750
## iter 80 value 111.202433
## final value 111.202337
## converged
## # weights: 121
## initial value 542.961146
## iter 10 value 166.950905
## iter 20 value 91.296277
## iter 30 value 69.961984
## iter 40 value 62.758711
## iter 50 value 59.420609
## iter 60 value 57.038624
## iter 70 value 55.251056
## iter 80 value 54.270606
## iter 90 value 53.959923
## iter 100 value 53.927938
## final value 53.927938
## stopped after 100 iterations
## # weights: 25
## initial value 515.267926
## iter 10 value 246.345973
## iter 20 value 213.836030
## iter 30 value 208.739181
## iter 40 value 204.212142
## iter 50 value 204.105596
## final value 204.105120
## converged
## # weights: 73
## initial value 625.309381
## iter 10 value 264.146252
## iter 20 value 217.948834
## iter 30 value 198.883808
## iter 40 value 176.291449
## iter 50 value 166.051322
## iter 60 value 164.630502
## iter 70 value 164.405111
## iter 80 value 163.375309
## iter 90 value 163.154796
## final value 163.154530
## converged
## # weights: 121
## initial value 599.183888
## iter 10 value 236.624956
## iter 20 value 167.261292
## iter 30 value 128.163768
## iter 40 value 114.274397
## iter 50 value 110.065972
## iter 60 value 97.949946
## iter 70 value 93.781613
## iter 80 value 93.222554
## iter 90 value 93.188822
## iter 100 value 93.185487
## final value 93.185487
## stopped after 100 iterations
## # weights: 25
## initial value 561.034656
## iter 10 value 258.618270
## iter 20 value 212.124442
## iter 30 value 194.052153
## iter 40 value 177.370931
## iter 50 value 174.933004
## iter 60 value 174.698747
## iter 70 value 174.651941
## iter 80 value 174.644548
## iter 90 value 172.886192
## iter 100 value 172.443430
## final value 172.443430
## stopped after 100 iterations
## # weights: 73
## initial value 552.988008
## iter 10 value 221.226391
## iter 20 value 177.487820
## iter 30 value 165.108687
## iter 40 value 156.678674
## iter 50 value 134.414061
## iter 60 value 124.006524
## iter 70 value 123.304463
## iter 80 value 123.174838
## iter 90 value 122.351418
## iter 100 value 122.019551
## final value 122.019551
## stopped after 100 iterations
## # weights: 121
## initial value 655.149948
## iter 10 value 215.334889
## iter 20 value 148.191549
## iter 30 value 100.220154
## iter 40 value 78.203750
## iter 50 value 59.669982
## iter 60 value 53.887870
## iter 70 value 50.365589
## iter 80 value 49.623528
## iter 90 value 49.427863
## iter 100 value 48.981994
## final value 48.981994
## stopped after 100 iterations
## # weights: 25
## initial value 521.395089
## iter 10 value 298.188677
## iter 20 value 227.343413
## iter 30 value 207.160393
## iter 40 value 193.771924
## iter 50 value 175.744701
## iter 60 value 174.513642
## iter 70 value 174.439729
## iter 80 value 174.417240
## iter 90 value 174.412429
## iter 100 value 174.411093
## final value 174.411093
## stopped after 100 iterations
## # weights: 73
## initial value 542.939486
## iter 10 value 201.122631
## iter 20 value 152.103509
## iter 30 value 122.539904
## iter 40 value 112.992128
## iter 50 value 111.537628
## iter 60 value 111.425610
## iter 70 value 111.417146
## iter 80 value 111.416119
## iter 90 value 111.415499
## iter 100 value 111.414872
## final value 111.414872
## stopped after 100 iterations
## # weights: 121
## initial value 505.063995
## iter 10 value 195.179821
## iter 20 value 138.949242
## iter 30 value 105.517771
## iter 40 value 95.531611
## iter 50 value 93.449423
## iter 60 value 91.974573
## iter 70 value 91.567722
## iter 80 value 91.023346
## iter 90 value 90.132920
## iter 100 value 89.713256
## final value 89.713256
## stopped after 100 iterations
## # weights: 25
## initial value 559.491374
## iter 10 value 236.028240
## iter 20 value 221.230015
## iter 30 value 215.652727
## iter 40 value 210.407669
## iter 50 value 208.772030
## iter 60 value 208.683838
## iter 60 value 208.683837
## iter 60 value 208.683837
## final value 208.683837
## converged
## # weights: 73
## initial value 579.569202
## iter 10 value 241.235821
## iter 20 value 201.161203
## iter 30 value 160.408181
## iter 40 value 150.538191
## iter 50 value 145.079949
## iter 60 value 143.857389
## iter 70 value 142.063201
## iter 80 value 132.961482
## iter 90 value 127.572778
## iter 100 value 125.252312
## final value 125.252312
## stopped after 100 iterations
## # weights: 121
## initial value 560.419386
## iter 10 value 183.624095
## iter 20 value 140.774385
## iter 30 value 130.152412
## iter 40 value 122.745435
## iter 50 value 118.598039
## iter 60 value 115.153661
## iter 70 value 109.489112
## iter 80 value 107.441487
## iter 90 value 102.719389
## iter 100 value 91.863748
## final value 91.863748
## stopped after 100 iterations
## # weights: 25
## initial value 488.627550
## iter 10 value 312.999827
## iter 20 value 272.858216
## iter 30 value 265.197504
## iter 40 value 262.641016
## iter 50 value 259.691543
## iter 60 value 252.911499
## iter 70 value 252.688698
## iter 80 value 252.578613
## iter 90 value 248.992990
## iter 100 value 248.958950
## final value 248.958950
## stopped after 100 iterations
## # weights: 73
## initial value 712.924277
## iter 10 value 256.041194
## iter 20 value 178.773677
## iter 30 value 136.959333
## iter 40 value 114.991123
## iter 50 value 110.118785
## iter 60 value 103.324028
## iter 70 value 100.700438
## iter 80 value 100.123497
## iter 90 value 98.877298
## iter 100 value 97.810751
## final value 97.810751
## stopped after 100 iterations
## # weights: 121
## initial value 516.686136
## iter 10 value 195.120076
## iter 20 value 95.899424
## iter 30 value 58.357049
## iter 40 value 42.613375
## iter 50 value 40.748384
## iter 60 value 40.057328
## iter 70 value 39.877527
## iter 80 value 39.658279
## iter 90 value 39.542782
## iter 100 value 39.507586
## final value 39.507586
## stopped after 100 iterations
## # weights: 121
## initial value 594.845601
## iter 10 value 201.099983
## iter 20 value 145.716833
## iter 30 value 122.488082
## iter 40 value 108.993378
## iter 50 value 100.363507
## iter 60 value 92.713815
## iter 70 value 89.727722
## iter 80 value 88.625205
## iter 90 value 87.353123
## iter 100 value 86.757997
## final value 86.757997
## stopped after 100 iterationsresultado_entrenamiento5 <- predict(modelo5, entrenamiento)
resultado_prueba5 <- predict(modelo5, prueba)
# Matriz de Confusion del Resultado del Entrenamiento1
mcre5 <- confusionMatrix(resultado_entrenamiento5, entrenamiento$target)
# Matriz de Confusion del Resultado de la Prueba1
mcrp5 <- confusionMatrix(resultado_prueba5, prueba$target)6. Modelo con el Metodo rf
modelo6 <- train(target ~. , data=entrenamiento,
method = "rf", #Cambiar,
preProcess= c("scale", "center"), #Existe el pre-procesamiento pero asi esta bn
trControl = trainControl(method="cv", number=10), #Cross Validation SIEMPRE
tuneGrid = expand.grid(mtry = c(2,4,6)) #Cambiar
)
resultado_entrenamiento6 <- predict(modelo6, entrenamiento)
resultado_prueba6 <- predict(modelo6, prueba)
# Matriz de Confusion del Resultado del Entrenamiento1
mcre6 <- confusionMatrix(resultado_entrenamiento6, entrenamiento$target)
# Matriz de Confusion del Resultado de la Prueba1
mcrp6 <- confusionMatrix(resultado_prueba6, prueba$target)Resumen de Resultados
resultados <- data.frame(
"svmLinear" = c(mcre1$overall["Accuracy"], mcrp1$overall["Accuracy"]), #overall es la tabla de la matriz
"svmRadial" = c(mcre2$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 0.8952497 1 0.8952497 0.8745432 0.9866017 1
## Precision de Prueba 0.8921569 1 0.8921569 0.8431373 1.0000000 1Conclusiones
Tras evaluar todos los modelos, podemos observar como svmRadial, nnet y rf presentan sobreajuste, ya que tienen una alta precision.
Acorde al resumen de resultados, el modelo mejor evaluado es el de svmLinear.
---
title: "CARET - Heart disease"
author: "Mariana Garcia A01177705"
date: "2024-08-21"
output: 
  html_document:
    toc: TRUE
    toc_float: TRUE
    code_download: TRUE
    theme: dark
---

```{r setup, include=FALSE} 
knitr::opts_chunk$set(warning = FALSE, message = FALSE) 
```

![](C:\\Users\\maria\\OneDrive\\Desktop\\AD24\\Modulo 2\\image_processing20210831-25365-zhus2m.gif)

# <span style="color: violet;">Teoria</span>
El paquete *CARET (Clafication and Regression Training)* es un paquete integral con una amplia variedad de algoritmos para el; aprendizaje automatico.

# <span style="color: violet;">Instalar paquetes y llamar librerias</span>
```{r warning=FALSE}
#install.packages("ggplot2") 
library(ggplot2) #Graficas con mejor diseno
#install.packages("lattice") 
library(lattice) #Crear graficos
#install.packages("caret") 
library(caret) #Algoritmos de aprendizaje automatico
#install.packages("datasets") 
library(datasets) #Usar la base de datos "Iris"
#install.packages("DataExplorer") 
library(DataExplorer) #Exploracion de datos
#install.packages("kernlab") 
library(kernlab) #Metodos de aprendizaje automatico
#install.packages("randomForest") 
library(randomForest) #Exploracion de datos

```

# <span style="color: violet;">Crear base de datos</span>
```{r}
df <- read.csv("C:\\Users\\maria\\OneDrive\\Desktop\\AD24\\Modulo 2\\heart.csv")
``` 

# <span style="color: violet;">Analisis Exploratorio</span>
```{r}
summary(df)
str(df)
plot_missing(df)

```

# <span style="color: violet;">Convertir variables a factores</span>
```{r}
df$sex <- as.factor(df$sex)
df$target <- as.factor(df$target) #variable que queremos predecir
df$cp <- as.factor(df$cp)
df$restecg <- as.factor(df$restecg)
df$ca <- as.factor(df$ca)
df$thal <- as.factor(df$thal)
df$exang <- as.factor(df$exang)
df$fbs <- as.factor(df$fbs)
df$slope <- as.factor(df$slope)


df
```

# <span style="color: violet;">Partir los datos 80-20</span>
```{r}
set.seed(123)
renglones_entremiento <- createDataPartition(df$target, p=0.8, list=FALSE)
entrenamiento <- df[renglones_entremiento, ]
prueba <- df[-renglones_entremiento, ]
```

# <span style="color: violet;">Metodos para Modelar</span>
Los metodos mas utilizados para modelar aprendizaje automatico son: 

* **SVM**: *Support Vector Machine* o Maquina de Vectores de Soporte. Hay varios subtipos: Lineal (svmLinear), Radial (svmRadial), Polinomico (svmPoly), etc.  
* **Arbol de Decision**: rpart
* **Redes Neuronales**: nnet
* **Random Forest**: o Bosques Aleatorios: rf

## <span style="color: violet;">1. Modelo con el Metodo svmLinear</span>
```{r}
modelo1 <- train(target ~. , data=entrenamiento,
                 method = "svmLinear", #Cambiar,
                 preProcess= c("scale", "center"), #Existe el pre-procesamiento pero asi esta bn
                 trControl = trainControl(method="cv", number=10), #Cross Validation SIEMPRE
                 tuneGrid = data.frame(C=1) #Cambiar
                 )

resultado_entrenamiento1 <- predict(modelo1, entrenamiento)
resultado_prueba1 <- predict(modelo1, prueba)

# Matriz de Confusion del Resultado del Entrenamiento1
mcre1 <- confusionMatrix(resultado_entrenamiento1, entrenamiento$target)

# Matriz de Confusion del Resultado de la Prueba1
mcrp1 <- confusionMatrix(resultado_prueba1, prueba$target)
```

## <span style="color: violet;">2. Modelo con el Metodo svmRadial</span>
```{r}
modelo2 <- train(target ~. , data=entrenamiento,
                 method = "svmRadial", #Cambiar,
                 preProcess= c("scale", "center"), #Existe el pre-procesamiento pero asi esta bn
                 trControl = trainControl(method="cv", number=10), #Cross Validation SIEMPRE
                 tuneGrid = data.frame(sigma=1, C=1) #Cambiar
                 )

resultado_entrenamiento2 <- predict(modelo2, entrenamiento)
resultado_prueba2 <- predict(modelo2, prueba)

# Matriz de Confusion del Resultado del Entrenamiento1
mcre2 <- confusionMatrix(resultado_entrenamiento2, entrenamiento$target)

# Matriz de Confusion del Resultado de la Prueba1
mcrp2 <- confusionMatrix(resultado_prueba2, prueba$target)
```

## <span style="color: violet;">3. Modelo con el Metodo svmPoly</span>
```{r}
modelo3 <- train(target ~. , data=entrenamiento,
                 method = "svmPoly", #Cambiar,
                 preProcess= c("scale", "center"), #Existe el pre-procesamiento pero asi esta bn
                 trControl = trainControl(method="cv", number=10), #Cross Validation SIEMPRE
                 tuneGrid = data.frame(degree=1, scale=1, C=1) #Cambiar
                 )

resultado_entrenamiento3 <- predict(modelo3, entrenamiento)
resultado_prueba3 <- predict(modelo3, prueba)

# Matriz de Confusion del Resultado del Entrenamiento1
mcre3 <- confusionMatrix(resultado_entrenamiento3, entrenamiento$target)

# Matriz de Confusion del Resultado de la Prueba1
mcrp3 <- confusionMatrix(resultado_prueba3, prueba$target)
```

## <span style="color: violet;">4. Modelo con el Metodo rpart</span>
```{r}
modelo4 <- train(target ~. , data=entrenamiento,
                 method = "rpart", #Cambiar,
                 preProcess= c("scale", "center"), #Existe el pre-procesamiento pero asi esta bn
                 trControl = trainControl(method="cv", number=10), #Cross Validation SIEMPRE
                 tuneLength = 10 #Cambiar
                 )

resultado_entrenamiento4 <- predict(modelo4, entrenamiento)
resultado_prueba4 <- predict(modelo4, prueba)

# Matriz de Confusion del Resultado del Entrenamiento1
mcre4 <- confusionMatrix(resultado_entrenamiento4, entrenamiento$target)

# Matriz de Confusion del Resultado de la Prueba1
mcrp4 <- confusionMatrix(resultado_prueba4, prueba$target)
```

## <span style="color: violet;">5. Modelo con el Metodo nnet</span>
```{r}
modelo5 <- train(target ~. , data=entrenamiento,
                 method = "nnet", #Cambiar,
                 preProcess= c("scale", "center"), #Existe el pre-procesamiento pero asi esta bn
                 trControl = trainControl(method="cv", number=10) #Cross Validation SIEMPRE
                  #Cambiar
                 )

resultado_entrenamiento5 <- predict(modelo5, entrenamiento)
resultado_prueba5 <- predict(modelo5, prueba)

# Matriz de Confusion del Resultado del Entrenamiento1
mcre5 <- confusionMatrix(resultado_entrenamiento5, entrenamiento$target)

# Matriz de Confusion del Resultado de la Prueba1
mcrp5 <- confusionMatrix(resultado_prueba5, prueba$target)
```


## <span style="color: violet;">6. Modelo con el Metodo rf</span>
```{r}
modelo6 <- train(target ~. , data=entrenamiento,
                 method = "rf", #Cambiar,
                 preProcess= c("scale", "center"), #Existe el pre-procesamiento pero asi esta bn
                 trControl = trainControl(method="cv", number=10), #Cross Validation SIEMPRE
                 tuneGrid = expand.grid(mtry = c(2,4,6)) #Cambiar
                 )

resultado_entrenamiento6 <- predict(modelo6, entrenamiento)
resultado_prueba6 <- predict(modelo6, prueba)

# Matriz de Confusion del Resultado del Entrenamiento1
mcre6 <- confusionMatrix(resultado_entrenamiento6, entrenamiento$target)

# Matriz de Confusion del Resultado de la Prueba1
mcrp6 <- confusionMatrix(resultado_prueba6, prueba$target)
```

# <span style="color: violet;">Resumen de Resultados</span>
```{r}
resultados <- data.frame(
  "svmLinear" = c(mcre1$overall["Accuracy"], mcrp1$overall["Accuracy"]), #overall es la tabla de la matriz
  "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
```

# <span style="color: violet;">Conclusiones</span>
Tras evaluar todos los modelos, podemos observar como *svmRadial*, *nnet* y *rf* presentan sobreajuste, ya que tienen una alta precision.

Acorde al resumen de resultados, el modelo mejor evaluado es el de **svmLinear**.