Caret - Heart
María Fernanda San Román Orozco - A01424691
26/08/2026
Teoría
El paquete CARET (Classification and Regression Training) es un paquete integral con una amplia variedad de algoritmos para el aprendizaje automático.
Instalar paquetes y llamar librerías
#install.packages("caret")
library("caret")
#install.packages("ggplot2")
library("ggplot2")
#install.packages("lattice")
library("lattice")
#install.packages("readxl")
library("readxl")
#install.packages("DataExplorer")
library("DataExplorer")
#install.packages("kernlab")
library("kernlab")
#install.packages("randomForest")
library("randomForest")Crear la Base de Datos
df = read_excel(file.choose())Entender la Base de Datos
df$target = as.factor(df$target)
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 0:499
## 1st Qu.:2.000 1:526
## Median :2.000
## Mean :2.324
## 3rd Qu.:3.000
## Max. :3.000str(df)## tibble [1,025 × 14] (S3: tbl_df/tbl/data.frame)
## $ age : num [1:1025] 52 53 70 61 62 58 58 55 46 54 ...
## $ sex : num [1:1025] 1 1 1 1 0 0 1 1 1 1 ...
## $ cp : num [1:1025] 0 0 0 0 0 0 0 0 0 0 ...
## $ trestbps: num [1:1025] 125 140 145 148 138 100 114 160 120 122 ...
## $ chol : num [1:1025] 212 203 174 203 294 248 318 289 249 286 ...
## $ fbs : num [1:1025] 0 1 0 0 1 0 0 0 0 0 ...
## $ restecg : num [1:1025] 1 0 1 1 1 0 2 0 0 0 ...
## $ thalach : num [1:1025] 168 155 125 161 106 122 140 145 144 116 ...
## $ exang : num [1:1025] 0 1 1 0 0 0 0 1 0 1 ...
## $ oldpeak : num [1:1025] 1 3.1 2.6 0 1.9 1 4.4 0.8 0.8 3.2 ...
## $ slope : num [1:1025] 2 0 0 2 1 1 0 1 2 1 ...
## $ ca : num [1:1025] 2 0 0 1 3 0 3 1 0 2 ...
## $ thal : num [1:1025] 3 3 3 3 2 2 1 3 3 2 ...
## $ target : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 1 1 1 ...#create_report(df)
plot_missing(df)
plot_histogram(df)
plot_correlation(df)
Nota: En modelos de clasificación, la variable que
queremos predecir debe tener formato de Factor
Partir la Base de Datos
# Normalmente 80-20 o 70-30
set.seed(123)
renglones_entrenamiento = createDataPartition(df$target, p=0.8, list=FALSE)
entrenamiento = df[renglones_entrenamiento, ]
prueba = df[-renglones_entrenamiento, ]Distintos tipos de Métodos para Modelar
Los métodos más utilizados para modelar aprendizaje automático son: * SVM: Support Vector Machine hay varios subtipos: Lineal (svmLinear), Radial (svmRadial), Polinómico (svmPoly), etc. * Árbol de Decisión: rpart * Redes Neuronales: nnet * Random Forest: rf
Modelo 1. SVM Lineal
modelo1 = train(target~., data=entrenamiento,
method="svmLinear", # Cambiar
preProcess = c("scale","center"),
trControl = trainControl(method="cv", number=10),
tuneGrid = data.frame(C=1) # Cambiar
)
resultado_entrenamiento1 = predict(modelo1,entrenamiento)
resultado_prueba1 = predict(modelo1,prueba)
mcre1 = confusionMatrix(resultado_entrenamiento1,entrenamiento$target)
mcre1## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 301 37
## 1 99 384
##
## Accuracy : 0.8343
## 95% CI : (0.8071, 0.8592)
## No Information Rate : 0.5128
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.6672
##
## Mcnemar's Test P-Value : 1.689e-07
##
## Sensitivity : 0.7525
## Specificity : 0.9121
## Pos Pred Value : 0.8905
## Neg Pred Value : 0.7950
## Prevalence : 0.4872
## Detection Rate : 0.3666
## Detection Prevalence : 0.4117
## Balanced Accuracy : 0.8323
##
## 'Positive' Class : 0
## mcrp1 = confusionMatrix(resultado_prueba1,prueba$target)
mcrp1## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 78 10
## 1 21 95
##
## Accuracy : 0.848
## 95% CI : (0.7913, 0.8944)
## No Information Rate : 0.5147
## P-Value [Acc > NIR] : < 2e-16
##
## Kappa : 0.6948
##
## Mcnemar's Test P-Value : 0.07249
##
## Sensitivity : 0.7879
## Specificity : 0.9048
## Pos Pred Value : 0.8864
## Neg Pred Value : 0.8190
## Prevalence : 0.4853
## Detection Rate : 0.3824
## Detection Prevalence : 0.4314
## Balanced Accuracy : 0.8463
##
## 'Positive' Class : 0
## Modelo 2. SVM Radial
modelo2 = train(target~., data=entrenamiento,
method="svmRadial", # Cambiar
preProcess = c("scale","center"),
trControl = trainControl(method="cv", number=10),
tuneGrid = data.frame(sigma=1, C=1) # Cambiar
)
resultado_entrenamiento2 = predict(modelo2,entrenamiento)
resultado_prueba2 = predict(modelo2,prueba)
mcre2 = confusionMatrix(resultado_entrenamiento2,entrenamiento$target)
mcre2## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 400 0
## 1 0 421
##
## Accuracy : 1
## 95% CI : (0.9955, 1)
## No Information Rate : 0.5128
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
##
## Mcnemar's Test P-Value : NA
##
## Sensitivity : 1.0000
## Specificity : 1.0000
## Pos Pred Value : 1.0000
## Neg Pred Value : 1.0000
## Prevalence : 0.4872
## Detection Rate : 0.4872
## Detection Prevalence : 0.4872
## Balanced Accuracy : 1.0000
##
## 'Positive' Class : 0
## mcrp2 = confusionMatrix(resultado_prueba2,prueba$target)
mcrp2## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 99 0
## 1 0 105
##
## Accuracy : 1
## 95% CI : (0.9821, 1)
## No Information Rate : 0.5147
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
##
## Mcnemar's Test P-Value : NA
##
## Sensitivity : 1.0000
## Specificity : 1.0000
## Pos Pred Value : 1.0000
## Neg Pred Value : 1.0000
## Prevalence : 0.4853
## Detection Rate : 0.4853
## Detection Prevalence : 0.4853
## Balanced Accuracy : 1.0000
##
## 'Positive' Class : 0
## Modelo 3. SVM Polinómico
modelo3 = train(target~., data=entrenamiento,
method="svmPoly", # Cambiar
preProcess = c("scale","center"),
trControl = trainControl(method="cv", number=10),
tuneGrid = data.frame(degree=1, scale=1, C=1) # Cambiar
)
resultado_entrenamiento3 = predict(modelo3,entrenamiento)
resultado_prueba3 = predict(modelo3,prueba)
mcre3 = confusionMatrix(resultado_entrenamiento3,entrenamiento$target)
mcre3## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 301 37
## 1 99 384
##
## Accuracy : 0.8343
## 95% CI : (0.8071, 0.8592)
## No Information Rate : 0.5128
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.6672
##
## Mcnemar's Test P-Value : 1.689e-07
##
## Sensitivity : 0.7525
## Specificity : 0.9121
## Pos Pred Value : 0.8905
## Neg Pred Value : 0.7950
## Prevalence : 0.4872
## Detection Rate : 0.3666
## Detection Prevalence : 0.4117
## Balanced Accuracy : 0.8323
##
## 'Positive' Class : 0
## mcrp3 = confusionMatrix(resultado_prueba3,prueba$target)
mcrp3## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 78 10
## 1 21 95
##
## Accuracy : 0.848
## 95% CI : (0.7913, 0.8944)
## No Information Rate : 0.5147
## P-Value [Acc > NIR] : < 2e-16
##
## Kappa : 0.6948
##
## Mcnemar's Test P-Value : 0.07249
##
## Sensitivity : 0.7879
## Specificity : 0.9048
## Pos Pred Value : 0.8864
## Neg Pred Value : 0.8190
## Prevalence : 0.4853
## Detection Rate : 0.3824
## Detection Prevalence : 0.4314
## Balanced Accuracy : 0.8463
##
## 'Positive' Class : 0
## Modelo 4. Árbol de Decisión
modelo4 = train(target~., data=entrenamiento,
method="rpart", # Cambiar
preProcess = c("scale","center"),
trControl = trainControl(method="cv", number=10),
tuneLength = 10 # Cambiar
)
resultado_entrenamiento4 = predict(modelo4,entrenamiento)
resultado_prueba4 = predict(modelo4,prueba)
mcre4 = confusionMatrix(resultado_entrenamiento4,entrenamiento$target)
mcre4## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 360 29
## 1 40 392
##
## Accuracy : 0.916
## 95% CI : (0.8948, 0.934)
## No Information Rate : 0.5128
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.8317
##
## Mcnemar's Test P-Value : 0.2286
##
## Sensitivity : 0.9000
## Specificity : 0.9311
## Pos Pred Value : 0.9254
## Neg Pred Value : 0.9074
## Prevalence : 0.4872
## Detection Rate : 0.4385
## Detection Prevalence : 0.4738
## Balanced Accuracy : 0.9156
##
## 'Positive' Class : 0
## mcrp4 = confusionMatrix(resultado_prueba4,prueba$target)
mcrp4## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 81 7
## 1 18 98
##
## Accuracy : 0.8775
## 95% CI : (0.8244, 0.9191)
## No Information Rate : 0.5147
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.7539
##
## Mcnemar's Test P-Value : 0.0455
##
## Sensitivity : 0.8182
## Specificity : 0.9333
## Pos Pred Value : 0.9205
## Neg Pred Value : 0.8448
## Prevalence : 0.4853
## Detection Rate : 0.3971
## Detection Prevalence : 0.4314
## Balanced Accuracy : 0.8758
##
## 'Positive' Class : 0
## Modelo 5. Random Forest
modelo5 = train(target~., data=entrenamiento,
method="rf", # Cambiar
preProcess = c("scale","center"),
trControl = trainControl(method="cv", number=10),
tuneLength = 10 # Cambiar
)
resultado_entrenamiento5 = predict(modelo5,entrenamiento)
resultado_prueba5 = predict(modelo5,prueba)
mcre5 = confusionMatrix(resultado_entrenamiento5,entrenamiento$target)
mcre5## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 400 0
## 1 0 421
##
## Accuracy : 1
## 95% CI : (0.9955, 1)
## No Information Rate : 0.5128
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
##
## Mcnemar's Test P-Value : NA
##
## Sensitivity : 1.0000
## Specificity : 1.0000
## Pos Pred Value : 1.0000
## Neg Pred Value : 1.0000
## Prevalence : 0.4872
## Detection Rate : 0.4872
## Detection Prevalence : 0.4872
## Balanced Accuracy : 1.0000
##
## 'Positive' Class : 0
## mcrp5 = confusionMatrix(resultado_prueba5,prueba$target)
mcrp5## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 99 0
## 1 0 105
##
## Accuracy : 1
## 95% CI : (0.9821, 1)
## No Information Rate : 0.5147
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
##
## Mcnemar's Test P-Value : NA
##
## Sensitivity : 1.0000
## Specificity : 1.0000
## Pos Pred Value : 1.0000
## Neg Pred Value : 1.0000
## Prevalence : 0.4853
## Detection Rate : 0.4853
## Detection Prevalence : 0.4853
## Balanced Accuracy : 1.0000
##
## 'Positive' Class : 0
## Modelo 6. Redes Neuronales
modelo6 = train(target~., data=entrenamiento,
method="nnet", # Cambiar
preProcess = c("scale","center"),
trControl = trainControl(method="cv", number=10))## # weights: 16
## initial value 510.881995
## iter 10 value 286.066804
## iter 20 value 258.638557
## iter 30 value 247.484348
## iter 40 value 246.944475
## iter 50 value 246.932587
## iter 60 value 246.929361
## iter 70 value 246.924032
## iter 80 value 246.922315
## iter 90 value 246.920972
## iter 100 value 246.918925
## final value 246.918925
## stopped after 100 iterations
## # weights: 46
## initial value 546.447273
## iter 10 value 253.501016
## iter 20 value 209.301283
## iter 30 value 192.463108
## iter 40 value 177.621101
## iter 50 value 166.318632
## iter 60 value 152.102490
## iter 70 value 131.892124
## iter 80 value 124.359822
## iter 90 value 123.883940
## iter 100 value 123.832599
## final value 123.832599
## stopped after 100 iterations
## # weights: 76
## initial value 578.728001
## iter 10 value 227.812649
## iter 20 value 152.642030
## iter 30 value 122.362142
## iter 40 value 114.574189
## iter 50 value 103.225927
## iter 60 value 99.597675
## iter 70 value 99.114044
## iter 80 value 99.049282
## iter 90 value 99.037323
## iter 100 value 99.036736
## final value 99.036736
## stopped after 100 iterations
## # weights: 16
## initial value 517.640916
## iter 10 value 293.031172
## iter 20 value 274.937751
## iter 30 value 273.581816
## iter 40 value 272.727901
## iter 50 value 272.011599
## iter 60 value 271.539253
## iter 70 value 271.473737
## iter 80 value 271.473669
## final value 271.473657
## converged
## # weights: 46
## initial value 555.488552
## iter 10 value 283.867731
## iter 20 value 227.243070
## iter 30 value 214.226246
## iter 40 value 211.411099
## iter 50 value 202.637416
## iter 60 value 199.842827
## iter 70 value 199.197299
## iter 80 value 199.171990
## final value 199.171838
## converged
## # weights: 76
## initial value 520.444458
## iter 10 value 229.289297
## iter 20 value 164.470385
## iter 30 value 148.732368
## iter 40 value 140.717631
## iter 50 value 137.072341
## iter 60 value 135.172266
## iter 70 value 132.779257
## iter 80 value 128.147686
## iter 90 value 127.345678
## iter 100 value 127.064417
## final value 127.064417
## stopped after 100 iterations
## # weights: 16
## initial value 561.297629
## iter 10 value 284.095589
## iter 20 value 260.885547
## iter 30 value 254.610549
## iter 40 value 249.480956
## iter 50 value 248.650261
## iter 60 value 248.591818
## iter 70 value 248.575751
## iter 80 value 248.559181
## iter 90 value 248.503540
## iter 100 value 248.447914
## final value 248.447914
## stopped after 100 iterations
## # weights: 46
## initial value 526.411433
## iter 10 value 274.774702
## iter 20 value 255.090516
## iter 30 value 234.582543
## iter 40 value 204.088195
## iter 50 value 192.898083
## iter 60 value 191.627115
## iter 70 value 190.001687
## iter 80 value 186.773961
## iter 90 value 178.967202
## iter 100 value 177.642819
## final value 177.642819
## stopped after 100 iterations
## # weights: 76
## initial value 638.618986
## iter 10 value 269.081216
## iter 20 value 196.784445
## iter 30 value 133.598616
## iter 40 value 105.126131
## iter 50 value 90.299126
## iter 60 value 80.760982
## iter 70 value 72.431196
## iter 80 value 71.290867
## iter 90 value 66.403392
## iter 100 value 65.874389
## final value 65.874389
## stopped after 100 iterations
## # weights: 16
## initial value 502.264796
## iter 10 value 326.540422
## iter 20 value 272.576209
## iter 30 value 261.653326
## iter 40 value 258.865074
## iter 50 value 251.432258
## iter 60 value 243.655206
## iter 70 value 240.307407
## final value 240.304562
## converged
## # weights: 46
## initial value 535.194005
## iter 10 value 244.224715
## iter 20 value 204.404151
## iter 30 value 192.300760
## iter 40 value 181.897485
## iter 50 value 174.474443
## iter 60 value 170.453503
## iter 70 value 166.373645
## iter 80 value 166.310667
## iter 90 value 166.297946
## iter 100 value 166.268450
## final value 166.268450
## stopped after 100 iterations
## # weights: 76
## initial value 558.366128
## iter 10 value 245.237914
## iter 20 value 167.478731
## iter 30 value 119.463397
## iter 40 value 105.585059
## iter 50 value 98.290713
## iter 60 value 97.131641
## iter 70 value 96.112121
## iter 80 value 95.742024
## iter 90 value 95.623233
## iter 100 value 95.469849
## final value 95.469849
## stopped after 100 iterations
## # weights: 16
## initial value 545.531494
## iter 10 value 297.836780
## iter 20 value 266.348057
## iter 30 value 259.840955
## iter 40 value 259.142031
## iter 50 value 259.046891
## final value 259.043061
## converged
## # weights: 46
## initial value 508.404506
## iter 10 value 242.638122
## iter 20 value 217.148214
## iter 30 value 194.768413
## iter 40 value 185.268534
## iter 50 value 183.363644
## iter 60 value 180.647740
## iter 70 value 180.440458
## iter 80 value 178.377895
## iter 90 value 176.898338
## iter 100 value 176.896343
## final value 176.896343
## stopped after 100 iterations
## # weights: 76
## initial value 512.052364
## iter 10 value 275.399676
## iter 20 value 223.271920
## iter 30 value 182.673453
## iter 40 value 158.148202
## iter 50 value 145.934625
## iter 60 value 140.735768
## iter 70 value 138.441669
## iter 80 value 137.587290
## iter 90 value 137.155303
## iter 100 value 136.929255
## final value 136.929255
## stopped after 100 iterations
## # weights: 16
## initial value 520.145796
## iter 10 value 283.956906
## iter 20 value 254.693912
## iter 30 value 249.930489
## iter 40 value 241.075472
## iter 50 value 240.652654
## iter 60 value 240.635925
## iter 70 value 240.633176
## iter 80 value 240.632609
## iter 90 value 240.629475
## iter 100 value 240.623286
## final value 240.623286
## stopped after 100 iterations
## # weights: 46
## initial value 542.865573
## iter 10 value 285.735606
## iter 20 value 230.191091
## iter 30 value 204.150683
## iter 40 value 192.461876
## iter 50 value 181.835431
## iter 60 value 168.259387
## iter 70 value 167.372542
## iter 80 value 167.258423
## iter 90 value 167.108058
## iter 100 value 165.735257
## final value 165.735257
## stopped after 100 iterations
## # weights: 76
## initial value 544.445288
## iter 10 value 248.115581
## iter 20 value 179.113237
## iter 30 value 120.598036
## iter 40 value 84.792066
## iter 50 value 73.116297
## iter 60 value 70.837860
## iter 70 value 68.991365
## iter 80 value 67.405522
## iter 90 value 67.181165
## iter 100 value 66.870911
## final value 66.870911
## stopped after 100 iterations
## # weights: 16
## initial value 531.202308
## iter 10 value 281.474441
## iter 20 value 259.810162
## iter 30 value 256.873261
## iter 40 value 251.727058
## iter 50 value 246.133440
## iter 60 value 224.922612
## final value 224.258076
## converged
## # weights: 46
## initial value 512.403451
## iter 10 value 276.132802
## iter 20 value 245.072616
## iter 30 value 209.537733
## iter 40 value 180.477119
## iter 50 value 164.986379
## iter 60 value 156.663490
## iter 70 value 154.938066
## iter 80 value 152.819562
## iter 90 value 149.272116
## iter 100 value 143.008002
## final value 143.008002
## stopped after 100 iterations
## # weights: 76
## initial value 537.395105
## iter 10 value 226.804611
## iter 20 value 127.170411
## iter 30 value 80.741273
## iter 40 value 66.215581
## iter 50 value 64.150023
## iter 60 value 64.078792
## iter 70 value 64.070896
## iter 80 value 64.070091
## iter 90 value 64.069785
## final value 64.069755
## converged
## # weights: 16
## initial value 497.638924
## iter 10 value 297.309617
## iter 20 value 271.815959
## iter 30 value 266.623275
## iter 40 value 262.982740
## iter 50 value 262.219341
## final value 262.202266
## converged
## # weights: 46
## initial value 522.678751
## iter 10 value 260.092659
## iter 20 value 217.031878
## iter 30 value 195.502700
## iter 40 value 192.130119
## iter 50 value 189.581482
## iter 60 value 185.361666
## iter 70 value 184.293131
## iter 80 value 183.863576
## iter 90 value 183.660230
## final value 183.659842
## converged
## # weights: 76
## initial value 521.192764
## iter 10 value 250.254983
## iter 20 value 201.924222
## iter 30 value 178.792616
## iter 40 value 168.455681
## iter 50 value 159.612525
## iter 60 value 156.500410
## iter 70 value 154.539770
## iter 80 value 151.038470
## iter 90 value 147.386407
## iter 100 value 146.928910
## final value 146.928910
## stopped after 100 iterations
## # weights: 16
## initial value 576.406040
## iter 10 value 262.017951
## iter 20 value 255.866854
## iter 30 value 241.763538
## iter 40 value 241.604385
## iter 50 value 241.499528
## iter 60 value 241.483626
## iter 70 value 241.458206
## iter 80 value 241.452748
## iter 90 value 241.451823
## iter 100 value 241.447568
## final value 241.447568
## stopped after 100 iterations
## # weights: 46
## initial value 521.279657
## iter 10 value 245.659749
## iter 20 value 202.295641
## iter 30 value 174.791008
## iter 40 value 162.464536
## iter 50 value 156.997698
## iter 60 value 152.827955
## iter 70 value 151.805811
## iter 80 value 151.282400
## iter 90 value 151.213635
## iter 100 value 151.135072
## final value 151.135072
## stopped after 100 iterations
## # weights: 76
## initial value 508.240677
## iter 10 value 207.025253
## iter 20 value 152.388126
## iter 30 value 125.822550
## iter 40 value 116.208248
## iter 50 value 114.022716
## iter 60 value 113.823568
## iter 70 value 113.595018
## iter 80 value 112.653169
## iter 90 value 112.548232
## iter 100 value 112.448583
## final value 112.448583
## stopped after 100 iterations
## # weights: 16
## initial value 514.848266
## iter 10 value 293.810938
## iter 20 value 267.931028
## iter 30 value 264.414466
## final value 264.387099
## converged
## # weights: 46
## initial value 514.031387
## iter 10 value 234.846187
## iter 20 value 203.730812
## iter 30 value 171.035408
## iter 40 value 151.392243
## iter 50 value 148.362500
## iter 60 value 148.311692
## iter 70 value 148.309265
## final value 148.309227
## converged
## # weights: 76
## initial value 523.961654
## iter 10 value 213.712720
## iter 20 value 156.030210
## iter 30 value 122.783538
## iter 40 value 99.633081
## iter 50 value 88.142365
## iter 60 value 86.356773
## iter 70 value 85.680218
## iter 80 value 85.605642
## iter 90 value 85.532232
## iter 100 value 85.494163
## final value 85.494163
## stopped after 100 iterations
## # weights: 16
## initial value 535.066444
## iter 10 value 318.853987
## iter 20 value 280.449851
## iter 30 value 271.459717
## iter 40 value 268.716382
## iter 50 value 268.615404
## final value 268.596763
## converged
## # weights: 46
## initial value 614.690691
## iter 10 value 293.219180
## iter 20 value 265.652639
## iter 30 value 247.338822
## iter 40 value 223.347456
## iter 50 value 216.191202
## iter 60 value 212.149695
## iter 70 value 205.193419
## iter 80 value 198.933908
## iter 90 value 198.476118
## iter 100 value 198.457116
## final value 198.457116
## stopped after 100 iterations
## # weights: 76
## initial value 554.962459
## iter 10 value 260.640393
## iter 20 value 204.535850
## iter 30 value 174.867993
## iter 40 value 161.782633
## iter 50 value 149.351413
## iter 60 value 140.972802
## iter 70 value 135.994284
## iter 80 value 133.657298
## iter 90 value 133.017435
## iter 100 value 132.469636
## final value 132.469636
## stopped after 100 iterations
## # weights: 16
## initial value 540.604629
## iter 10 value 271.599996
## iter 20 value 264.906331
## iter 30 value 263.166802
## iter 40 value 262.495052
## iter 50 value 262.446020
## iter 60 value 262.226180
## iter 70 value 262.191530
## iter 80 value 262.182386
## iter 90 value 262.172410
## iter 100 value 262.166767
## final value 262.166767
## stopped after 100 iterations
## # weights: 46
## initial value 539.887633
## iter 10 value 232.994944
## iter 20 value 196.918742
## iter 30 value 188.283628
## iter 40 value 172.759221
## iter 50 value 163.467064
## iter 60 value 162.489892
## iter 70 value 161.658652
## iter 80 value 161.239738
## iter 90 value 161.173858
## iter 100 value 160.786069
## final value 160.786069
## stopped after 100 iterations
## # weights: 76
## initial value 581.409801
## iter 10 value 238.475160
## iter 20 value 196.544519
## iter 30 value 132.722604
## iter 40 value 83.354733
## iter 50 value 65.863739
## iter 60 value 59.537392
## iter 70 value 57.535366
## iter 80 value 56.106232
## iter 90 value 54.850270
## iter 100 value 54.234719
## final value 54.234719
## stopped after 100 iterations
## # weights: 16
## initial value 597.975250
## iter 10 value 357.257754
## iter 20 value 330.858914
## iter 30 value 317.015919
## iter 40 value 315.207605
## iter 50 value 311.556509
## iter 60 value 302.339048
## final value 302.309107
## converged
## # weights: 46
## initial value 487.766910
## iter 10 value 242.021354
## iter 20 value 206.268010
## iter 30 value 190.127344
## iter 40 value 175.125492
## iter 50 value 167.216822
## iter 60 value 166.543603
## iter 70 value 166.461745
## iter 80 value 166.459443
## iter 90 value 166.457182
## iter 100 value 166.455504
## final value 166.455504
## stopped after 100 iterations
## # weights: 76
## initial value 534.085687
## iter 10 value 212.058080
## iter 20 value 136.218296
## iter 30 value 114.345659
## iter 40 value 99.424595
## iter 50 value 90.742452
## iter 60 value 87.358494
## iter 70 value 86.767652
## iter 80 value 86.730019
## iter 90 value 86.726828
## iter 100 value 86.726502
## final value 86.726502
## stopped after 100 iterations
## # weights: 16
## initial value 518.779141
## iter 10 value 272.564520
## iter 20 value 266.793293
## iter 30 value 266.072567
## final value 266.072528
## converged
## # weights: 46
## initial value 525.437427
## iter 10 value 299.934120
## iter 20 value 229.756284
## iter 30 value 206.430729
## iter 40 value 196.719070
## iter 50 value 194.696764
## iter 60 value 193.984114
## iter 70 value 193.686441
## iter 80 value 193.666115
## iter 90 value 193.579117
## final value 193.574709
## converged
## # weights: 76
## initial value 571.184755
## iter 10 value 243.185678
## iter 20 value 200.444327
## iter 30 value 172.412892
## iter 40 value 156.643462
## iter 50 value 148.705232
## iter 60 value 144.513149
## iter 70 value 141.303476
## iter 80 value 140.456837
## iter 90 value 138.860324
## iter 100 value 137.567327
## final value 137.567327
## stopped after 100 iterations
## # weights: 16
## initial value 511.824937
## iter 10 value 286.271427
## iter 20 value 253.933038
## iter 30 value 250.843038
## iter 40 value 247.766515
## iter 50 value 247.596726
## iter 60 value 247.450696
## iter 70 value 247.403540
## iter 80 value 247.367603
## iter 90 value 247.304326
## iter 100 value 247.220816
## final value 247.220816
## stopped after 100 iterations
## # weights: 46
## initial value 656.606706
## iter 10 value 250.153249
## iter 20 value 221.212317
## iter 30 value 200.311418
## iter 40 value 187.324427
## iter 50 value 172.520469
## iter 60 value 169.792632
## iter 70 value 169.425590
## iter 80 value 169.287685
## iter 90 value 169.166578
## iter 100 value 168.971533
## final value 168.971533
## stopped after 100 iterations
## # weights: 76
## initial value 544.831366
## iter 10 value 242.565245
## iter 20 value 136.037487
## iter 30 value 99.240611
## iter 40 value 92.793144
## iter 50 value 91.104020
## iter 60 value 85.072196
## iter 70 value 84.094739
## iter 80 value 83.481275
## iter 90 value 82.985194
## iter 100 value 82.348632
## final value 82.348632
## stopped after 100 iterations
## # weights: 16
## initial value 513.703169
## iter 10 value 346.208213
## iter 20 value 260.367470
## iter 30 value 253.689507
## iter 40 value 252.729526
## iter 50 value 244.665953
## iter 60 value 240.477461
## final value 240.467479
## converged
## # weights: 46
## initial value 525.262472
## iter 10 value 261.030478
## iter 20 value 212.305518
## iter 30 value 174.534056
## iter 40 value 156.280782
## iter 50 value 145.915226
## iter 60 value 143.945283
## iter 70 value 142.813525
## iter 80 value 141.985019
## iter 90 value 141.556759
## iter 100 value 141.396691
## final value 141.396691
## stopped after 100 iterations
## # weights: 76
## initial value 504.709516
## iter 10 value 242.014366
## iter 20 value 182.918762
## iter 30 value 128.168376
## iter 40 value 101.224285
## iter 50 value 96.585067
## iter 60 value 92.694428
## iter 70 value 91.844336
## iter 80 value 90.080380
## iter 90 value 83.018138
## iter 100 value 82.602548
## final value 82.602548
## stopped after 100 iterations
## # weights: 16
## initial value 505.829998
## iter 10 value 282.215827
## iter 20 value 269.890956
## iter 30 value 266.574994
## iter 40 value 265.806190
## iter 50 value 264.344408
## iter 60 value 263.976451
## iter 60 value 263.976449
## iter 60 value 263.976449
## final value 263.976449
## converged
## # weights: 46
## initial value 539.864862
## iter 10 value 255.494584
## iter 20 value 218.071763
## iter 30 value 207.786297
## iter 40 value 187.732913
## iter 50 value 167.929651
## iter 60 value 162.210629
## iter 70 value 161.103410
## iter 80 value 160.971822
## iter 90 value 160.963263
## iter 90 value 160.963261
## iter 90 value 160.963261
## final value 160.963261
## converged
## # weights: 76
## initial value 484.016739
## iter 10 value 219.044708
## iter 20 value 165.648933
## iter 30 value 141.703937
## iter 40 value 122.035379
## iter 50 value 114.877639
## iter 60 value 109.593410
## iter 70 value 107.274121
## iter 80 value 106.551598
## iter 90 value 106.354106
## iter 100 value 106.327326
## final value 106.327326
## stopped after 100 iterations
## # weights: 16
## initial value 494.180334
## iter 10 value 335.449829
## iter 20 value 270.792035
## iter 30 value 255.721745
## iter 40 value 254.131959
## iter 50 value 252.241135
## iter 60 value 251.524636
## iter 70 value 251.413865
## iter 80 value 251.391047
## iter 90 value 250.979463
## final value 250.975891
## converged
## # weights: 46
## initial value 610.911827
## iter 10 value 247.789738
## iter 20 value 218.275710
## iter 30 value 192.710030
## iter 40 value 173.576041
## iter 50 value 149.171574
## iter 60 value 136.913227
## iter 70 value 134.887404
## iter 80 value 133.946146
## iter 90 value 133.211687
## iter 100 value 131.294552
## final value 131.294552
## stopped after 100 iterations
## # weights: 76
## initial value 541.633245
## iter 10 value 205.978760
## iter 20 value 114.062797
## iter 30 value 65.354388
## iter 40 value 57.555214
## iter 50 value 57.170177
## iter 60 value 56.972322
## iter 70 value 56.819552
## iter 80 value 56.774301
## iter 90 value 56.732192
## iter 100 value 56.692789
## final value 56.692789
## stopped after 100 iterations
## # weights: 16
## initial value 512.411701
## iter 10 value 363.237472
## iter 20 value 274.939005
## iter 30 value 261.469834
## iter 40 value 260.850976
## iter 50 value 258.888839
## iter 60 value 254.514892
## iter 70 value 241.332102
## iter 80 value 239.758079
## iter 90 value 238.709406
## iter 100 value 237.911307
## final value 237.911307
## stopped after 100 iterations
## # weights: 46
## initial value 523.221203
## iter 10 value 257.795489
## iter 20 value 191.265728
## iter 30 value 161.568018
## iter 40 value 147.870214
## iter 50 value 137.358427
## iter 60 value 134.029196
## iter 70 value 133.637720
## iter 80 value 133.098184
## iter 90 value 132.996311
## iter 100 value 132.908296
## final value 132.908296
## stopped after 100 iterations
## # weights: 76
## initial value 537.352356
## iter 10 value 244.227908
## iter 20 value 169.533555
## iter 30 value 122.128747
## iter 40 value 100.875311
## iter 50 value 87.793309
## iter 60 value 82.551529
## iter 70 value 81.368555
## iter 80 value 81.209543
## iter 90 value 81.169054
## iter 100 value 81.150434
## final value 81.150434
## stopped after 100 iterations
## # weights: 16
## initial value 544.350766
## iter 10 value 296.604431
## iter 20 value 273.856477
## iter 30 value 269.340449
## iter 40 value 267.778764
## iter 50 value 266.653584
## iter 60 value 266.224987
## iter 60 value 266.224986
## iter 60 value 266.224986
## final value 266.224986
## converged
## # weights: 46
## initial value 553.341773
## iter 10 value 265.871527
## iter 20 value 235.496099
## iter 30 value 209.670482
## iter 40 value 205.291017
## iter 50 value 204.728672
## iter 60 value 204.666218
## iter 70 value 204.657175
## final value 204.656802
## converged
## # weights: 76
## initial value 532.326576
## iter 10 value 238.547362
## iter 20 value 168.688194
## iter 30 value 154.840399
## iter 40 value 136.095853
## iter 50 value 126.220045
## iter 60 value 122.611827
## iter 70 value 119.941329
## iter 80 value 119.093220
## iter 90 value 118.748307
## iter 100 value 118.718341
## final value 118.718341
## stopped after 100 iterations
## # weights: 16
## initial value 504.135256
## iter 10 value 287.021114
## iter 20 value 269.351941
## iter 30 value 261.963776
## iter 40 value 261.751714
## iter 50 value 261.710209
## iter 60 value 261.516114
## iter 70 value 261.495954
## final value 261.495737
## converged
## # weights: 46
## initial value 540.271907
## iter 10 value 281.312706
## iter 20 value 248.059672
## iter 30 value 230.485562
## iter 40 value 212.642449
## iter 50 value 200.122888
## iter 60 value 190.541956
## iter 70 value 186.318424
## iter 80 value 184.469188
## iter 90 value 181.970083
## iter 100 value 180.757744
## final value 180.757744
## stopped after 100 iterations
## # weights: 76
## initial value 523.697093
## iter 10 value 229.751428
## iter 20 value 130.540761
## iter 30 value 89.785078
## iter 40 value 82.499313
## iter 50 value 80.145004
## iter 60 value 78.208280
## iter 70 value 77.465692
## iter 80 value 76.721052
## iter 90 value 75.002012
## iter 100 value 73.251313
## final value 73.251313
## stopped after 100 iterations
## # weights: 16
## initial value 563.954725
## iter 10 value 269.216446
## iter 20 value 261.051286
## iter 30 value 260.153708
## iter 40 value 259.145607
## iter 50 value 257.365051
## iter 60 value 255.260213
## iter 70 value 254.960413
## iter 80 value 254.776532
## iter 90 value 254.228029
## iter 100 value 254.127811
## final value 254.127811
## stopped after 100 iterations
## # weights: 46
## initial value 556.439828
## iter 10 value 239.862092
## iter 20 value 205.467000
## iter 30 value 178.070790
## iter 40 value 153.750876
## iter 50 value 140.943616
## iter 60 value 127.749591
## iter 70 value 124.990868
## iter 80 value 121.219135
## iter 90 value 118.157803
## iter 100 value 116.466620
## final value 116.466620
## stopped after 100 iterations
## # weights: 76
## initial value 580.380336
## iter 10 value 244.035540
## iter 20 value 168.306168
## iter 30 value 93.022863
## iter 40 value 56.577624
## iter 50 value 45.852548
## iter 60 value 43.959042
## iter 70 value 43.374091
## iter 80 value 42.975097
## iter 90 value 42.624347
## iter 100 value 42.521292
## final value 42.521292
## stopped after 100 iterations
## # weights: 16
## initial value 510.351384
## iter 10 value 325.527731
## iter 20 value 273.156509
## iter 30 value 265.287982
## iter 40 value 263.695842
## iter 50 value 262.287729
## final value 262.168952
## converged
## # weights: 46
## initial value 513.695933
## iter 10 value 247.679897
## iter 20 value 210.501891
## iter 30 value 194.652812
## iter 40 value 188.918466
## iter 50 value 187.593219
## iter 60 value 186.884374
## iter 70 value 185.410017
## iter 80 value 185.243685
## final value 185.243664
## converged
## # weights: 76
## initial value 523.833371
## iter 10 value 284.856055
## iter 20 value 235.241051
## iter 30 value 208.496036
## iter 40 value 192.957167
## iter 50 value 187.623374
## iter 60 value 184.448486
## iter 70 value 183.547287
## iter 80 value 183.370688
## iter 90 value 183.273070
## iter 100 value 183.240740
## final value 183.240740
## stopped after 100 iterations
## # weights: 16
## initial value 525.744139
## iter 10 value 313.543157
## iter 20 value 271.711106
## iter 30 value 262.954494
## iter 40 value 259.495129
## iter 50 value 249.383666
## iter 60 value 245.723171
## iter 70 value 245.446988
## iter 80 value 245.347806
## iter 90 value 245.313940
## iter 100 value 245.313083
## final value 245.313083
## stopped after 100 iterations
## # weights: 46
## initial value 483.501058
## iter 10 value 233.731622
## iter 20 value 184.894959
## iter 30 value 161.762216
## iter 40 value 152.031840
## iter 50 value 149.433468
## iter 60 value 148.828634
## iter 70 value 148.264604
## iter 80 value 147.978098
## iter 90 value 147.820764
## iter 100 value 147.640457
## final value 147.640457
## stopped after 100 iterations
## # weights: 76
## initial value 523.131471
## iter 10 value 241.451241
## iter 20 value 182.405077
## iter 30 value 138.901662
## iter 40 value 104.449354
## iter 50 value 95.675981
## iter 60 value 92.247430
## iter 70 value 91.677873
## iter 80 value 91.402268
## iter 90 value 91.044322
## iter 100 value 90.435384
## final value 90.435384
## stopped after 100 iterations
## # weights: 16
## initial value 526.911982
## iter 10 value 296.507978
## iter 20 value 259.145126
## iter 30 value 254.637805
## iter 40 value 253.355283
## iter 50 value 249.608036
## iter 60 value 232.885329
## iter 70 value 231.215668
## iter 80 value 231.211229
## iter 90 value 231.207826
## final value 231.207269
## converged
## # weights: 46
## initial value 546.001078
## iter 10 value 269.948107
## iter 20 value 214.827134
## iter 30 value 181.230537
## iter 40 value 166.123948
## iter 50 value 143.687397
## iter 60 value 127.838994
## iter 70 value 123.425458
## iter 80 value 122.265491
## iter 90 value 122.137540
## iter 100 value 121.933049
## final value 121.933049
## stopped after 100 iterations
## # weights: 76
## initial value 542.321798
## iter 10 value 225.763617
## iter 20 value 137.061947
## iter 30 value 75.256744
## iter 40 value 54.233112
## iter 50 value 45.056488
## iter 60 value 43.137094
## iter 70 value 42.640066
## iter 80 value 42.508375
## iter 90 value 42.400593
## iter 100 value 42.365866
## final value 42.365866
## stopped after 100 iterations
## # weights: 16
## initial value 519.870084
## iter 10 value 316.840797
## iter 20 value 263.120313
## iter 30 value 258.995950
## final value 258.987141
## converged
## # weights: 46
## initial value 477.309863
## iter 10 value 236.412202
## iter 20 value 214.615498
## iter 30 value 196.767186
## iter 40 value 183.089304
## iter 50 value 178.094699
## iter 60 value 173.726807
## iter 70 value 173.584824
## final value 173.583202
## converged
## # weights: 76
## initial value 507.573439
## iter 10 value 228.331141
## iter 20 value 189.915605
## iter 30 value 177.041681
## iter 40 value 163.790842
## iter 50 value 160.714798
## iter 60 value 158.455872
## iter 70 value 152.185639
## iter 80 value 150.333849
## iter 90 value 149.748341
## iter 100 value 147.352747
## final value 147.352747
## stopped after 100 iterations
## # weights: 16
## initial value 519.264313
## iter 10 value 303.700228
## iter 20 value 259.549445
## iter 30 value 257.603268
## iter 40 value 257.205680
## iter 50 value 257.121537
## iter 60 value 256.942802
## iter 70 value 256.922670
## iter 80 value 256.921432
## iter 90 value 256.920698
## final value 256.920655
## converged
## # weights: 46
## initial value 535.488332
## iter 10 value 241.671387
## iter 20 value 211.529483
## iter 30 value 167.924206
## iter 40 value 138.984229
## iter 50 value 121.724731
## iter 60 value 116.664238
## iter 70 value 116.304458
## iter 80 value 115.584533
## iter 90 value 115.009003
## iter 100 value 114.726704
## final value 114.726704
## stopped after 100 iterations
## # weights: 76
## initial value 536.202697
## iter 10 value 278.870403
## iter 20 value 213.033708
## iter 30 value 167.041537
## iter 40 value 139.327105
## iter 50 value 123.140594
## iter 60 value 114.628487
## iter 70 value 111.591552
## iter 80 value 106.095404
## iter 90 value 102.486301
## iter 100 value 100.763596
## final value 100.763596
## stopped after 100 iterations
## # weights: 16
## initial value 521.249811
## iter 10 value 271.698621
## iter 20 value 270.560260
## iter 30 value 270.105766
## iter 40 value 268.828702
## iter 50 value 264.749425
## iter 60 value 263.522104
## iter 70 value 263.440542
## iter 80 value 263.270812
## iter 90 value 263.138668
## iter 100 value 263.133265
## final value 263.133265
## stopped after 100 iterations
## # weights: 46
## initial value 542.949407
## iter 10 value 274.660939
## iter 20 value 221.785328
## iter 30 value 207.678994
## iter 40 value 197.215785
## iter 50 value 191.184114
## iter 60 value 190.890986
## iter 70 value 190.886003
## iter 80 value 190.885603
## final value 190.885600
## converged
## # weights: 76
## initial value 501.230955
## iter 10 value 231.336380
## iter 20 value 153.322861
## iter 30 value 92.116870
## iter 40 value 73.132748
## iter 50 value 69.528435
## iter 60 value 68.238206
## iter 70 value 67.485303
## iter 80 value 67.007727
## iter 90 value 66.910416
## iter 100 value 66.847981
## final value 66.847981
## stopped after 100 iterations
## # weights: 16
## initial value 520.022611
## iter 10 value 329.516322
## iter 20 value 300.577369
## iter 30 value 289.931678
## iter 40 value 279.455562
## iter 50 value 274.735098
## iter 60 value 272.297614
## iter 70 value 271.980780
## iter 80 value 271.980364
## final value 271.980177
## converged
## # weights: 46
## initial value 508.934699
## iter 10 value 262.298715
## iter 20 value 216.183438
## iter 30 value 200.155906
## iter 40 value 189.818886
## iter 50 value 187.685511
## iter 60 value 186.837441
## iter 70 value 186.710956
## final value 186.710863
## converged
## # weights: 76
## initial value 552.384444
## iter 10 value 233.962533
## iter 20 value 194.972423
## iter 30 value 159.308527
## iter 40 value 142.620052
## iter 50 value 136.641654
## iter 60 value 131.051580
## iter 70 value 126.283360
## iter 80 value 124.240656
## iter 90 value 121.446316
## iter 100 value 117.612138
## final value 117.612138
## stopped after 100 iterations
## # weights: 16
## initial value 607.130977
## iter 10 value 478.135226
## iter 20 value 402.817310
## iter 30 value 384.833138
## iter 40 value 377.851432
## iter 50 value 335.410918
## iter 60 value 313.616554
## iter 70 value 307.869721
## iter 80 value 298.821150
## iter 90 value 295.037167
## iter 100 value 294.352411
## final value 294.352411
## stopped after 100 iterations
## # weights: 46
## initial value 572.424786
## iter 10 value 245.197991
## iter 20 value 204.050773
## iter 30 value 188.492887
## iter 40 value 168.543308
## iter 50 value 152.345383
## iter 60 value 148.891329
## iter 70 value 148.293016
## iter 80 value 147.171657
## iter 90 value 146.909624
## iter 100 value 146.765242
## final value 146.765242
## stopped after 100 iterations
## # weights: 76
## initial value 479.008375
## iter 10 value 232.850509
## iter 20 value 157.325265
## iter 30 value 112.019487
## iter 40 value 89.891438
## iter 50 value 86.290548
## iter 60 value 85.042369
## iter 70 value 84.364300
## iter 80 value 83.653906
## iter 90 value 83.032863
## iter 100 value 78.226855
## final value 78.226855
## stopped after 100 iterations
## # weights: 76
## initial value 651.859869
## iter 10 value 248.434089
## iter 20 value 168.225953
## iter 30 value 147.774764
## iter 40 value 137.916475
## iter 50 value 131.882683
## iter 60 value 130.973669
## iter 70 value 129.150579
## iter 80 value 129.102816
## iter 90 value 129.078521
## iter 100 value 129.078086
## final value 129.078086
## stopped after 100 iterationsresultado_entrenamiento6 = predict(modelo6,entrenamiento)
resultado_prueba6 = predict(modelo6,prueba)
mcre6 = confusionMatrix(resultado_entrenamiento6,entrenamiento$target)
mcre6## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 354 10
## 1 46 411
##
## Accuracy : 0.9318
## 95% CI : (0.9123, 0.9481)
## No Information Rate : 0.5128
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8632
##
## Mcnemar's Test P-Value : 2.91e-06
##
## Sensitivity : 0.8850
## Specificity : 0.9762
## Pos Pred Value : 0.9725
## Neg Pred Value : 0.8993
## Prevalence : 0.4872
## Detection Rate : 0.4312
## Detection Prevalence : 0.4434
## Balanced Accuracy : 0.9306
##
## 'Positive' Class : 0
## mcrp6 = confusionMatrix(resultado_prueba6,prueba$target)
mcrp6## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 83 3
## 1 16 102
##
## Accuracy : 0.9069
## 95% CI : (0.8584, 0.943)
## No Information Rate : 0.5147
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8129
##
## Mcnemar's Test P-Value : 0.005905
##
## Sensitivity : 0.8384
## Specificity : 0.9714
## Pos Pred Value : 0.9651
## Neg Pred Value : 0.8644
## Prevalence : 0.4853
## Detection Rate : 0.4069
## Detection Prevalence : 0.4216
## Balanced Accuracy : 0.9049
##
## 'Positive' Class : 0
## Tabla de Resultados
resultados = data.frame(
"smvLinear" = c(mcre1$overall["Accuracy"], mcrp1$overall["Accuracy"]),
"smvRadial" = c(mcre2$overall["Accuracy"], mcrp2$overall["Accuracy"]),
"smvPoly" = c(mcre3$overall["Accuracy"], mcrp3$overall["Accuracy"]),
"rpart" = c(mcre4$overall["Accuracy"], mcrp4$overall["Accuracy"]),
"rf" = c(mcre5$overall["Accuracy"], mcrp5$overall["Accuracy"]),
"nnet" = c(mcre6$overall["Accuracy"], mcrp6$overall["Accuracy"])
)
rownames(resultados) = c("Exactitud del Entrenamiento", "Exactitud de la Prueba")
resultados## smvLinear smvRadial smvPoly rpart rf
## Exactitud del Entrenamiento 0.8343484 1 0.8343484 0.9159562 1
## Exactitud de la Prueba 0.8480392 1 0.8480392 0.8774510 1
## nnet
## Exactitud del Entrenamiento 0.9317905
## Exactitud de la Prueba 0.9068627Conclusión
En conclusión, se recomienda usar el modelo SVM Radial o el de Random Forest ya que obtuvimos la mayor exactitud en el conjunto de prueba, con un 100%, por lo tanto, ambos presentan el mejor desempeño para la clasificación de enfermedad cardiaca.
---
title: "Caret - Heart"
author: "María Fernanda San Román Orozco - A01424691"
date: "26/08/2026"
output: 
  html_document:
    toc: TRUE
    toc_float: TRUE
    code_download: TRUE
    theme: sandstone
---
![](https://media2.giphy.com/media/v1.Y2lkPTZjMDliOTUycG00eDI5aDhyaGJqY3VkMDBwZHF1NjZzbGo0amMwNnRmYTUxNHBkbiZlcD12MV9naWZzX3NlYXJjaCZjdD1n/buPHaNvgiNdCGPnrbG/200w.gif)

# <span style="color:red">Teoría </span>
El paquete **CARET** (Classification and Regression Training) es un paquete integral con una amplia variedad de algoritmos para el aprendizaje automático.

# <span style="color:red">Instalar paquetes y llamar librerías </span>
```{r message=FALSE, warning=FALSE}
#install.packages("caret")
library("caret")
#install.packages("ggplot2")
library("ggplot2")
#install.packages("lattice")
library("lattice")
#install.packages("readxl")
library("readxl")
#install.packages("DataExplorer")
library("DataExplorer")
#install.packages("kernlab")
library("kernlab")
#install.packages("randomForest")
library("randomForest")
```

# <span style="color:red">Crear la Base de Datos </span>
```{r}
df = read_excel(file.choose())
```

# <span style="color:red">Entender la Base de Datos </span>
```{r}
df$target = as.factor(df$target)
summary(df)
str(df)
#create_report(df)
plot_missing(df)
plot_histogram(df)
plot_correlation(df)
```
**Nota**: En modelos de clasificación, la variable que queremos predecir debe tener formato de Factor

# <span style="color:red">Partir la Base de Datos </span>
```{r}
# Normalmente 80-20 o 70-30
set.seed(123)
renglones_entrenamiento = createDataPartition(df$target, p=0.8, list=FALSE)
entrenamiento = df[renglones_entrenamiento, ]
prueba = df[-renglones_entrenamiento, ]
```

# <span style="color:red">Distintos tipos de Métodos para Modelar </span>
Los métodos más utilizados para modelar aprendizaje automático son:
* **SVM**: Support Vector Machine hay varios subtipos: Lineal (svmLinear), Radial (svmRadial), Polinómico (svmPoly), etc.
* **Árbol de Decisión**: rpart
* **Redes Neuronales**: nnet
* **Random Forest**: rf

# <span style="color:red">Modelo 1. SVM Lineal </span>
```{r}
modelo1 = train(target~., data=entrenamiento,
                method="svmLinear", # Cambiar
                preProcess = c("scale","center"),
                trControl = trainControl(method="cv", number=10),
                tuneGrid = data.frame(C=1) # Cambiar
                )

resultado_entrenamiento1 = predict(modelo1,entrenamiento)
resultado_prueba1 = predict(modelo1,prueba)

mcre1 = confusionMatrix(resultado_entrenamiento1,entrenamiento$target)
mcre1

mcrp1 = confusionMatrix(resultado_prueba1,prueba$target)
mcrp1
```

# <span style="color:red">Modelo 2. SVM Radial </span>
```{r}
modelo2 = train(target~., data=entrenamiento,
                method="svmRadial", # Cambiar
                preProcess = c("scale","center"),
                trControl = trainControl(method="cv", number=10),
                tuneGrid = data.frame(sigma=1, C=1) # Cambiar
                )

resultado_entrenamiento2 = predict(modelo2,entrenamiento)
resultado_prueba2 = predict(modelo2,prueba)

mcre2 = confusionMatrix(resultado_entrenamiento2,entrenamiento$target)
mcre2

mcrp2 = confusionMatrix(resultado_prueba2,prueba$target)
mcrp2
```

# <span style="color:red">Modelo 3. SVM Polinómico </span>
```{r}
modelo3 = train(target~., data=entrenamiento,
                method="svmPoly", # Cambiar
                preProcess = c("scale","center"),
                trControl = trainControl(method="cv", number=10),
                tuneGrid = data.frame(degree=1, scale=1, C=1) # Cambiar
                )

resultado_entrenamiento3 = predict(modelo3,entrenamiento)
resultado_prueba3 = predict(modelo3,prueba)

mcre3 = confusionMatrix(resultado_entrenamiento3,entrenamiento$target)
mcre3

mcrp3 = confusionMatrix(resultado_prueba3,prueba$target)
mcrp3
```

# <span style="color:red">Modelo 4. Árbol de Decisión </span>
```{r}
modelo4 = train(target~., data=entrenamiento,
                method="rpart", # Cambiar
                preProcess = c("scale","center"),
                trControl = trainControl(method="cv", number=10),
                tuneLength = 10 # Cambiar
                )

resultado_entrenamiento4 = predict(modelo4,entrenamiento)
resultado_prueba4 = predict(modelo4,prueba)

mcre4 = confusionMatrix(resultado_entrenamiento4,entrenamiento$target)
mcre4

mcrp4 = confusionMatrix(resultado_prueba4,prueba$target)
mcrp4
```

# <span style="color:red">Modelo 5. Random Forest </span>
```{r}
modelo5 = train(target~., data=entrenamiento,
                method="rf", # Cambiar
                preProcess = c("scale","center"),
                trControl = trainControl(method="cv", number=10),
                tuneLength = 10 # Cambiar
                )

resultado_entrenamiento5 = predict(modelo5,entrenamiento)
resultado_prueba5 = predict(modelo5,prueba)

mcre5 = confusionMatrix(resultado_entrenamiento5,entrenamiento$target)
mcre5

mcrp5 = confusionMatrix(resultado_prueba5,prueba$target)
mcrp5
```

# <span style="color:red">Modelo 6. Redes Neuronales </span>
```{r message=FALSE, warning=FALSE}
modelo6 = train(target~., data=entrenamiento,
                method="nnet", # Cambiar
                preProcess = c("scale","center"),
                trControl = trainControl(method="cv", number=10))

resultado_entrenamiento6 = predict(modelo6,entrenamiento)
resultado_prueba6 = predict(modelo6,prueba)

mcre6 = confusionMatrix(resultado_entrenamiento6,entrenamiento$target)
mcre6

mcrp6 = confusionMatrix(resultado_prueba6,prueba$target)
mcrp6
```

# <span style="color:red">Tabla de Resultados </span>
```{r}
resultados = data.frame(
  "smvLinear" = c(mcre1$overall["Accuracy"], mcrp1$overall["Accuracy"]),
  "smvRadial" = c(mcre2$overall["Accuracy"], mcrp2$overall["Accuracy"]),
  "smvPoly" = c(mcre3$overall["Accuracy"], mcrp3$overall["Accuracy"]),
  "rpart" = c(mcre4$overall["Accuracy"], mcrp4$overall["Accuracy"]),
  "rf" = c(mcre5$overall["Accuracy"], mcrp5$overall["Accuracy"]),
  "nnet" = c(mcre6$overall["Accuracy"], mcrp6$overall["Accuracy"])
)
rownames(resultados) = c("Exactitud del Entrenamiento", "Exactitud de la Prueba")
resultados
```

# <span style="color:red">Conclusión </span>
En conclusión, se recomienda usar el modelo SVM Radial o el de Random Forest ya que obtuvimos la mayor exactitud en el conjunto de prueba, con un 100%, por lo tanto, ambos presentan el mejor desempeño para la clasificación de enfermedad cardiaca.