library(caret)
## Loading required package: ggplot2
## Warning: package 'ggplot2' was built under R version 4.2.3
## Loading required package: lattice
library(ggplot2) # Crear gráficos
library(datasets) # Usar la base de datos "Iris"
library(lattice) # Crear gráficos
library(DataExplorer)
## Warning: package 'DataExplorer' was built under R version 4.2.3
library(mlbench)
data(BreastCancer)
<- data.frame(BreastCancer) df
summary(df)
## Id Cl.thickness Cell.size Cell.shape Marg.adhesion
## Length:699 1 :145 1 :384 1 :353 1 :407
## Class :character 5 :130 10 : 67 2 : 59 2 : 58
## Mode :character 3 :108 3 : 52 10 : 58 3 : 58
## 4 : 80 2 : 45 3 : 56 10 : 55
## 10 : 69 4 : 40 4 : 44 4 : 33
## 2 : 50 5 : 30 5 : 34 8 : 25
## (Other):117 (Other): 81 (Other): 95 (Other): 63
## Epith.c.size Bare.nuclei Bl.cromatin Normal.nucleoli Mitoses
## 2 :386 1 :402 2 :166 1 :443 1 :579
## 3 : 72 10 :132 3 :165 10 : 61 2 : 35
## 4 : 48 2 : 30 1 :152 3 : 44 3 : 33
## 1 : 47 5 : 30 7 : 73 2 : 36 10 : 14
## 6 : 41 3 : 28 4 : 40 8 : 24 4 : 12
## 5 : 39 (Other): 61 5 : 34 6 : 22 7 : 9
## (Other): 66 NA's : 16 (Other): 69 (Other): 69 (Other): 17
## Class
## benign :458
## malignant:241
##
##
##
##
##
str(df)
## 'data.frame': 699 obs. of 11 variables:
## $ Id : chr "1000025" "1002945" "1015425" "1016277" ...
## $ Cl.thickness : Ord.factor w/ 10 levels "1"<"2"<"3"<"4"<..: 5 5 3 6 4 8 1 2 2 4 ...
## $ Cell.size : Ord.factor w/ 10 levels "1"<"2"<"3"<"4"<..: 1 4 1 8 1 10 1 1 1 2 ...
## $ Cell.shape : Ord.factor w/ 10 levels "1"<"2"<"3"<"4"<..: 1 4 1 8 1 10 1 2 1 1 ...
## $ Marg.adhesion : Ord.factor w/ 10 levels "1"<"2"<"3"<"4"<..: 1 5 1 1 3 8 1 1 1 1 ...
## $ Epith.c.size : Ord.factor w/ 10 levels "1"<"2"<"3"<"4"<..: 2 7 2 3 2 7 2 2 2 2 ...
## $ Bare.nuclei : Factor w/ 10 levels "1","2","3","4",..: 1 10 2 4 1 10 10 1 1 1 ...
## $ Bl.cromatin : Factor w/ 10 levels "1","2","3","4",..: 3 3 3 3 3 9 3 3 1 2 ...
## $ Normal.nucleoli: Factor w/ 10 levels "1","2","3","4",..: 1 2 1 7 1 7 1 1 1 1 ...
## $ Mitoses : Factor w/ 9 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 5 1 ...
## $ Class : Factor w/ 2 levels "benign","malignant": 1 1 1 1 1 2 1 1 1 1 ...
# Limpieza de datos
$Id <- NULL
df
#Limpieza
$Cl.thickness <- as.numeric(df$Cl.thickness)
df$Cell.size <- as.numeric(df$Cell.size)
df$Cell.shape <- as.numeric(df$Cell.shape)
df$Marg.adhesion <- as.numeric(df$Marg.adhesion)
df$Epith.c.size <- as.numeric(df$Epith.c.size)
df$Bare.nuclei <- as.numeric(df$Bare.nuclei)
df$Bl.cromatin <- as.numeric(df$Bl.cromatin)
df$Normal.nucleoli <- as.numeric(df$Normal.nucleoli)
df$Mitoses <- as.numeric(df$Mitoses)
df$Class <- as.factor(df$Class)
df
#Quitar 16 nulos
<- na.omit(df)
df plot_missing(df)
plot_histogram(df)
plot_correlation(df)
La variable debe ser un factor
set.seed(123)
<-createDataPartition(df$Class, p=0.8, list=FALSE)
renglones_entrenamiento <- df[renglones_entrenamiento, ]
entrenamiento
<- df[-renglones_entrenamiento, ] prueba
<- train(Class ~ ., data= entrenamiento, method = "svmLinear", preProcess= c("scale", "center"), trControl = trainControl(method = "cv", number = 10), tuneGrid = data.frame(C=1)) #Cuando es svmLinear
modelo1
<- predict(modelo1, entrenamiento)
resultado_entrenamiento1
<- predict(modelo1, prueba) resultado_prueba1
<- confusionMatrix(resultado_entrenamiento1, entrenamiento$Class)
mcre1
mcre1
## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 347 7
## malignant 9 185
##
## Accuracy : 0.9708
## 95% CI : (0.953, 0.9832)
## No Information Rate : 0.6496
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.936
##
## Mcnemar's Test P-Value : 0.8026
##
## Sensitivity : 0.9747
## Specificity : 0.9635
## Pos Pred Value : 0.9802
## Neg Pred Value : 0.9536
## Prevalence : 0.6496
## Detection Rate : 0.6332
## Detection Prevalence : 0.6460
## Balanced Accuracy : 0.9691
##
## 'Positive' Class : benign
##
<- confusionMatrix(resultado_prueba1, prueba$Class)
mcrp1
mcrp1
## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 87 2
## malignant 1 45
##
## Accuracy : 0.9778
## 95% CI : (0.9364, 0.9954)
## No Information Rate : 0.6519
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.9508
##
## Mcnemar's Test P-Value : 1
##
## Sensitivity : 0.9886
## Specificity : 0.9574
## Pos Pred Value : 0.9775
## Neg Pred Value : 0.9783
## Prevalence : 0.6519
## Detection Rate : 0.6444
## Detection Prevalence : 0.6593
## Balanced Accuracy : 0.9730
##
## 'Positive' Class : benign
##
<- train(Class ~ ., data= entrenamiento, method = "svmRadial", preProcess= c("scale", "center"), trControl = trainControl(method = "cv", number = 10), tuneGrid = data.frame(sigma=1, C=1)) #Cambiar
modelo2
<- predict(modelo2, entrenamiento)
resultado_entrenamiento2
<- predict(modelo2, prueba) resultado_prueba2
<- confusionMatrix(resultado_entrenamiento2, entrenamiento$Class)
mcre2
mcre2
## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 354 0
## malignant 2 192
##
## Accuracy : 0.9964
## 95% CI : (0.9869, 0.9996)
## No Information Rate : 0.6496
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.992
##
## Mcnemar's Test P-Value : 0.4795
##
## Sensitivity : 0.9944
## Specificity : 1.0000
## Pos Pred Value : 1.0000
## Neg Pred Value : 0.9897
## Prevalence : 0.6496
## Detection Rate : 0.6460
## Detection Prevalence : 0.6460
## Balanced Accuracy : 0.9972
##
## 'Positive' Class : benign
##
<- confusionMatrix(resultado_prueba2, prueba$Class)
mcrp2
mcrp2
## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 82 0
## malignant 6 47
##
## Accuracy : 0.9556
## 95% CI : (0.9058, 0.9835)
## No Information Rate : 0.6519
## P-Value [Acc > NIR] : < 2e-16
##
## Kappa : 0.9049
##
## Mcnemar's Test P-Value : 0.04123
##
## Sensitivity : 0.9318
## Specificity : 1.0000
## Pos Pred Value : 1.0000
## Neg Pred Value : 0.8868
## Prevalence : 0.6519
## Detection Rate : 0.6074
## Detection Prevalence : 0.6074
## Balanced Accuracy : 0.9659
##
## 'Positive' Class : benign
##
<- train(Class ~ ., data= entrenamiento, method = "svmPoly", preProcess= c("scale", "center"), trControl = trainControl(method = "cv", number = 10), tuneGrid = data.frame(degree=1,scale=1, C=1)) #Cambiar
modelo3
<- predict(modelo3, entrenamiento)
resultado_entrenamiento3
<- predict(modelo3, prueba) resultado_prueba3
<- confusionMatrix(resultado_entrenamiento3, entrenamiento$Class)
mcre3
mcre3
## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 347 7
## malignant 9 185
##
## Accuracy : 0.9708
## 95% CI : (0.953, 0.9832)
## No Information Rate : 0.6496
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.936
##
## Mcnemar's Test P-Value : 0.8026
##
## Sensitivity : 0.9747
## Specificity : 0.9635
## Pos Pred Value : 0.9802
## Neg Pred Value : 0.9536
## Prevalence : 0.6496
## Detection Rate : 0.6332
## Detection Prevalence : 0.6460
## Balanced Accuracy : 0.9691
##
## 'Positive' Class : benign
##
<- confusionMatrix(resultado_prueba3, prueba$Class)
mcrp3
mcrp3
## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 87 2
## malignant 1 45
##
## Accuracy : 0.9778
## 95% CI : (0.9364, 0.9954)
## No Information Rate : 0.6519
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.9508
##
## Mcnemar's Test P-Value : 1
##
## Sensitivity : 0.9886
## Specificity : 0.9574
## Pos Pred Value : 0.9775
## Neg Pred Value : 0.9783
## Prevalence : 0.6519
## Detection Rate : 0.6444
## Detection Prevalence : 0.6593
## Balanced Accuracy : 0.9730
##
## 'Positive' Class : benign
##
<- train(Class ~ ., data = entrenamiento, method = "rpart", preProcess = c("scale", "center"), trControl = trainControl(method="cv", number = 10), tuneLength = 10)
modelo4
<- predict(modelo4, entrenamiento)
resultado_entrenamiento4
<- predict(modelo4, prueba) resultado_prueba4
<- confusionMatrix(resultado_entrenamiento4, entrenamiento$Class)
mcre4
mcre4
## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 345 9
## malignant 11 183
##
## Accuracy : 0.9635
## 95% CI : (0.9442, 0.9776)
## No Information Rate : 0.6496
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.92
##
## Mcnemar's Test P-Value : 0.8231
##
## Sensitivity : 0.9691
## Specificity : 0.9531
## Pos Pred Value : 0.9746
## Neg Pred Value : 0.9433
## Prevalence : 0.6496
## Detection Rate : 0.6296
## Detection Prevalence : 0.6460
## Balanced Accuracy : 0.9611
##
## 'Positive' Class : benign
##
<- confusionMatrix(resultado_prueba4, prueba$Class)
mcrp4
mcrp4
## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 87 5
## malignant 1 42
##
## Accuracy : 0.9556
## 95% CI : (0.9058, 0.9835)
## No Information Rate : 0.6519
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.9001
##
## Mcnemar's Test P-Value : 0.2207
##
## Sensitivity : 0.9886
## Specificity : 0.8936
## Pos Pred Value : 0.9457
## Neg Pred Value : 0.9767
## Prevalence : 0.6519
## Detection Rate : 0.6444
## Detection Prevalence : 0.6815
## Balanced Accuracy : 0.9411
##
## 'Positive' Class : benign
##
<- train(Class ~ ., data = entrenamiento, method = "nnet", preProcess = c("scale", "center"), trControl = trainControl(method="cv", number = 10)) modelo5
## # weights: 12
## initial value 376.477149
## iter 10 value 42.437195
## iter 20 value 37.383594
## iter 30 value 37.366404
## iter 40 value 36.703788
## iter 50 value 32.802253
## iter 60 value 32.783185
## iter 70 value 32.780576
## iter 80 value 32.780352
## iter 90 value 32.780267
## iter 100 value 32.779932
## final value 32.779932
## stopped after 100 iterations
## # weights: 34
## initial value 398.967389
## iter 10 value 41.580064
## iter 20 value 32.819841
## iter 30 value 30.580631
## iter 40 value 30.171660
## iter 50 value 29.018950
## iter 60 value 26.270274
## iter 70 value 25.101933
## iter 80 value 24.289118
## iter 90 value 23.970739
## iter 100 value 23.966875
## final value 23.966875
## stopped after 100 iterations
## # weights: 56
## initial value 392.380535
## iter 10 value 36.130217
## iter 20 value 16.520603
## iter 30 value 7.379376
## iter 40 value 4.979779
## iter 50 value 1.881996
## iter 60 value 1.413954
## iter 70 value 1.397568
## iter 80 value 1.389585
## iter 90 value 1.386721
## iter 100 value 1.386333
## final value 1.386333
## stopped after 100 iterations
## # weights: 12
## initial value 350.410635
## iter 10 value 74.916798
## iter 20 value 58.878074
## iter 30 value 49.960077
## iter 40 value 49.661904
## final value 49.661892
## converged
## # weights: 34
## initial value 349.539635
## iter 10 value 115.137963
## iter 20 value 45.339839
## iter 30 value 41.959631
## iter 40 value 38.444785
## iter 50 value 37.928941
## iter 60 value 37.672511
## iter 70 value 37.662808
## final value 37.662804
## converged
## # weights: 56
## initial value 385.799685
## iter 10 value 41.550941
## iter 20 value 37.775498
## iter 30 value 37.351510
## iter 40 value 37.203503
## iter 50 value 36.881480
## iter 60 value 36.816477
## iter 70 value 36.815808
## iter 80 value 36.815763
## iter 80 value 36.815763
## final value 36.815763
## converged
## # weights: 12
## initial value 411.250561
## iter 10 value 128.481791
## iter 20 value 48.775343
## iter 30 value 48.108500
## iter 40 value 45.635617
## iter 50 value 42.789717
## iter 60 value 42.603333
## iter 70 value 39.693478
## iter 80 value 39.677257
## iter 90 value 39.671032
## iter 100 value 39.658964
## final value 39.658964
## stopped after 100 iterations
## # weights: 34
## initial value 396.907245
## iter 10 value 36.935107
## iter 20 value 27.845747
## iter 30 value 24.064004
## iter 40 value 22.507661
## iter 50 value 22.161343
## iter 60 value 21.912002
## iter 70 value 21.436296
## iter 80 value 21.176512
## iter 90 value 21.115843
## iter 100 value 20.994476
## final value 20.994476
## stopped after 100 iterations
## # weights: 56
## initial value 470.209121
## iter 10 value 36.854692
## iter 20 value 29.775951
## iter 30 value 26.512592
## iter 40 value 23.490629
## iter 50 value 21.090972
## iter 60 value 14.528117
## iter 70 value 14.147383
## iter 80 value 13.015758
## iter 90 value 12.840415
## iter 100 value 12.812813
## final value 12.812813
## stopped after 100 iterations
## # weights: 12
## initial value 355.508036
## iter 10 value 52.186983
## iter 20 value 49.731412
## iter 30 value 42.751850
## iter 40 value 42.589636
## iter 50 value 40.970318
## iter 60 value 39.558646
## iter 70 value 39.543586
## iter 80 value 39.515130
## iter 90 value 39.503451
## iter 100 value 39.499587
## final value 39.499587
## stopped after 100 iterations
## # weights: 34
## initial value 345.293855
## iter 10 value 38.850419
## iter 20 value 35.299467
## iter 30 value 29.063227
## iter 40 value 25.318280
## iter 50 value 22.886781
## iter 60 value 21.721975
## iter 70 value 21.281768
## iter 80 value 21.101518
## iter 90 value 21.071842
## iter 100 value 21.066335
## final value 21.066335
## stopped after 100 iterations
## # weights: 56
## initial value 318.370065
## iter 10 value 40.055119
## iter 20 value 24.416365
## iter 30 value 13.343905
## iter 40 value 12.497316
## iter 50 value 12.101419
## iter 60 value 11.714407
## iter 70 value 10.361632
## iter 80 value 10.093093
## iter 90 value 9.858132
## iter 100 value 9.611812
## final value 9.611812
## stopped after 100 iterations
## # weights: 12
## initial value 383.405588
## iter 10 value 70.986072
## iter 20 value 54.771996
## iter 30 value 53.597555
## iter 40 value 53.544775
## iter 40 value 53.544774
## iter 40 value 53.544774
## final value 53.544774
## converged
## # weights: 34
## initial value 437.860910
## iter 10 value 52.917571
## iter 20 value 47.485871
## iter 30 value 43.339800
## iter 40 value 42.347250
## iter 50 value 41.987058
## iter 60 value 41.759375
## iter 70 value 41.757165
## iter 70 value 41.757165
## final value 41.757165
## converged
## # weights: 56
## initial value 432.938183
## iter 10 value 53.241057
## iter 20 value 42.793023
## iter 30 value 40.750430
## iter 40 value 40.140010
## iter 50 value 40.065188
## iter 60 value 40.059476
## iter 70 value 40.058576
## final value 40.058575
## converged
## # weights: 12
## initial value 370.559464
## iter 10 value 56.330575
## iter 20 value 45.487568
## iter 30 value 40.718222
## iter 40 value 39.656038
## iter 50 value 39.640343
## iter 60 value 39.639341
## iter 70 value 39.638382
## iter 80 value 39.638099
## iter 90 value 39.638035
## iter 100 value 39.637985
## final value 39.637985
## stopped after 100 iterations
## # weights: 34
## initial value 371.184689
## iter 10 value 42.069486
## iter 20 value 36.888133
## iter 30 value 35.233768
## iter 40 value 31.583862
## iter 50 value 29.951261
## iter 60 value 27.882599
## iter 70 value 27.350586
## iter 80 value 27.191210
## iter 90 value 27.150219
## iter 100 value 26.998852
## final value 26.998852
## stopped after 100 iterations
## # weights: 56
## initial value 324.010663
## iter 10 value 35.285669
## iter 20 value 18.983575
## iter 30 value 8.864095
## iter 40 value 8.493163
## iter 50 value 8.373325
## iter 60 value 5.023711
## iter 70 value 4.414094
## iter 80 value 4.245991
## iter 90 value 4.032582
## iter 100 value 2.658630
## final value 2.658630
## stopped after 100 iterations
## # weights: 12
## initial value 376.965522
## iter 10 value 57.458884
## iter 20 value 48.268436
## iter 30 value 45.450565
## iter 40 value 43.475756
## iter 50 value 39.525989
## iter 60 value 39.499126
## iter 70 value 39.494123
## iter 80 value 39.484240
## iter 90 value 39.481187
## iter 100 value 39.480996
## final value 39.480996
## stopped after 100 iterations
## # weights: 34
## initial value 461.169324
## iter 10 value 37.143796
## iter 20 value 28.209150
## iter 30 value 19.639361
## iter 40 value 17.612668
## iter 50 value 17.529577
## iter 60 value 17.528701
## final value 17.528675
## converged
## # weights: 56
## initial value 291.377968
## iter 10 value 34.872750
## iter 20 value 27.020476
## iter 30 value 21.365333
## iter 40 value 18.322587
## iter 50 value 14.852247
## iter 60 value 14.005387
## iter 70 value 13.813872
## iter 80 value 13.634998
## iter 90 value 13.585523
## iter 100 value 13.561454
## final value 13.561454
## stopped after 100 iterations
## # weights: 12
## initial value 350.760977
## iter 10 value 62.985282
## iter 20 value 52.810932
## iter 30 value 52.778785
## final value 52.777991
## converged
## # weights: 34
## initial value 325.655243
## iter 10 value 53.894733
## iter 20 value 47.675190
## iter 30 value 45.276035
## iter 40 value 43.160908
## iter 50 value 41.825807
## iter 60 value 41.617544
## iter 70 value 41.610425
## iter 80 value 41.555818
## iter 90 value 41.553218
## iter 90 value 41.553218
## iter 90 value 41.553218
## final value 41.553218
## converged
## # weights: 56
## initial value 290.669028
## iter 10 value 85.091956
## iter 20 value 49.886743
## iter 30 value 44.222060
## iter 40 value 40.735343
## iter 50 value 39.664872
## iter 60 value 39.313892
## iter 70 value 38.909658
## iter 80 value 38.818642
## iter 90 value 38.811901
## iter 100 value 38.811641
## final value 38.811641
## stopped after 100 iterations
## # weights: 12
## initial value 429.343504
## iter 10 value 48.000220
## iter 20 value 43.121829
## iter 30 value 41.515601
## iter 40 value 37.669596
## iter 50 value 37.396040
## iter 60 value 37.383183
## iter 70 value 37.341077
## iter 80 value 37.328979
## iter 90 value 37.320610
## iter 100 value 37.293722
## final value 37.293722
## stopped after 100 iterations
## # weights: 34
## initial value 476.188548
## iter 10 value 48.622911
## iter 20 value 32.034715
## iter 30 value 28.036310
## iter 40 value 24.999756
## iter 50 value 23.129264
## iter 60 value 22.975342
## iter 70 value 22.913586
## iter 80 value 22.892855
## iter 90 value 22.858722
## iter 100 value 22.833042
## final value 22.833042
## stopped after 100 iterations
## # weights: 56
## initial value 378.900773
## iter 10 value 36.248260
## iter 20 value 20.146364
## iter 30 value 11.327904
## iter 40 value 9.748409
## iter 50 value 9.432211
## iter 60 value 9.055354
## iter 70 value 8.966362
## iter 80 value 8.935854
## iter 90 value 8.917093
## iter 100 value 8.902201
## final value 8.902201
## stopped after 100 iterations
## # weights: 12
## initial value 344.806105
## iter 10 value 53.314981
## iter 20 value 51.864827
## iter 30 value 48.439608
## iter 40 value 47.074809
## iter 50 value 45.171586
## iter 60 value 45.010562
## iter 70 value 44.965893
## iter 80 value 44.919082
## iter 90 value 44.853411
## iter 100 value 44.822481
## final value 44.822481
## stopped after 100 iterations
## # weights: 34
## initial value 324.352768
## iter 10 value 33.754090
## iter 20 value 26.301046
## iter 30 value 21.302920
## iter 40 value 20.470135
## iter 50 value 20.022692
## iter 60 value 19.903231
## iter 70 value 19.895252
## iter 80 value 19.894923
## iter 90 value 19.894423
## final value 19.894412
## converged
## # weights: 56
## initial value 301.048227
## iter 10 value 31.006045
## iter 20 value 26.243828
## iter 30 value 20.745469
## iter 40 value 18.745179
## iter 50 value 18.149268
## iter 60 value 17.213758
## iter 70 value 17.126430
## iter 80 value 17.035218
## iter 90 value 16.949961
## iter 100 value 16.783764
## final value 16.783764
## stopped after 100 iterations
## # weights: 12
## initial value 441.401693
## iter 10 value 52.177398
## iter 20 value 47.978570
## iter 30 value 46.688911
## final value 46.686683
## converged
## # weights: 34
## initial value 400.891990
## iter 10 value 38.167635
## iter 20 value 36.493121
## iter 30 value 36.422004
## iter 40 value 36.355602
## iter 50 value 36.351007
## final value 36.351006
## converged
## # weights: 56
## initial value 354.480594
## iter 10 value 40.204286
## iter 20 value 36.276416
## iter 30 value 34.831402
## iter 40 value 34.638085
## iter 50 value 34.632346
## final value 34.630418
## converged
## # weights: 12
## initial value 382.278414
## iter 10 value 51.491238
## iter 20 value 42.900868
## iter 30 value 36.397646
## iter 40 value 36.365532
## iter 50 value 36.354776
## iter 60 value 36.352952
## iter 70 value 36.351207
## iter 80 value 36.350648
## iter 90 value 36.350404
## iter 100 value 36.350228
## final value 36.350228
## stopped after 100 iterations
## # weights: 34
## initial value 385.229559
## iter 10 value 48.761838
## iter 20 value 34.609357
## iter 30 value 22.131388
## iter 40 value 17.743321
## iter 50 value 16.603891
## iter 60 value 16.337974
## iter 70 value 16.142630
## iter 80 value 16.099927
## iter 90 value 16.066784
## iter 100 value 16.036454
## final value 16.036454
## stopped after 100 iterations
## # weights: 56
## initial value 517.073810
## iter 10 value 208.699725
## iter 20 value 22.379999
## iter 30 value 16.405697
## iter 40 value 14.761128
## iter 50 value 14.203133
## iter 60 value 13.855094
## iter 70 value 13.643576
## iter 80 value 13.000648
## iter 90 value 10.708189
## iter 100 value 10.152099
## final value 10.152099
## stopped after 100 iterations
## # weights: 12
## initial value 292.895306
## iter 10 value 52.551393
## iter 20 value 47.795990
## iter 30 value 43.326449
## iter 40 value 42.498683
## iter 50 value 42.483639
## iter 60 value 42.481460
## iter 70 value 42.480221
## iter 80 value 42.478425
## iter 90 value 42.476811
## iter 100 value 42.476437
## final value 42.476437
## stopped after 100 iterations
## # weights: 34
## initial value 370.482153
## iter 10 value 38.851578
## iter 20 value 33.185345
## iter 30 value 26.837372
## iter 40 value 23.550785
## iter 50 value 22.282349
## iter 60 value 21.741716
## iter 70 value 21.617553
## iter 80 value 21.606068
## iter 90 value 21.594047
## iter 100 value 21.590763
## final value 21.590763
## stopped after 100 iterations
## # weights: 56
## initial value 331.163435
## iter 10 value 45.763128
## iter 20 value 29.174052
## iter 30 value 22.347069
## iter 40 value 16.843208
## iter 50 value 15.554057
## iter 60 value 15.069029
## iter 70 value 14.686885
## iter 80 value 14.392322
## iter 90 value 14.005408
## iter 100 value 13.840468
## final value 13.840468
## stopped after 100 iterations
## # weights: 12
## initial value 445.911318
## iter 10 value 50.664074
## iter 20 value 49.103146
## iter 30 value 48.764748
## final value 48.764740
## converged
## # weights: 34
## initial value 539.171803
## iter 10 value 71.174738
## iter 20 value 44.888487
## iter 30 value 40.773068
## iter 40 value 39.291940
## iter 50 value 38.948505
## iter 60 value 38.805208
## iter 70 value 38.789757
## iter 80 value 38.777548
## iter 90 value 38.777242
## final value 38.777240
## converged
## # weights: 56
## initial value 300.526929
## iter 10 value 92.670644
## iter 20 value 45.607100
## iter 30 value 38.482565
## iter 40 value 37.620450
## iter 50 value 37.333679
## iter 60 value 37.305632
## iter 70 value 37.304888
## final value 37.304880
## converged
## # weights: 12
## initial value 342.113667
## iter 10 value 39.784475
## iter 20 value 37.502481
## iter 30 value 37.235549
## iter 40 value 37.094419
## iter 50 value 36.355221
## iter 60 value 36.235421
## iter 70 value 35.963827
## iter 80 value 35.822521
## iter 90 value 35.822367
## iter 100 value 35.821303
## final value 35.821303
## stopped after 100 iterations
## # weights: 34
## initial value 379.416717
## iter 10 value 42.835820
## iter 20 value 38.625630
## iter 30 value 33.091455
## iter 40 value 32.424501
## iter 50 value 31.958173
## iter 60 value 31.780375
## iter 70 value 31.571448
## iter 80 value 31.478999
## iter 90 value 31.464766
## iter 100 value 31.375798
## final value 31.375798
## stopped after 100 iterations
## # weights: 56
## initial value 369.465817
## iter 10 value 36.101780
## iter 20 value 17.316087
## iter 30 value 10.213775
## iter 40 value 7.378137
## iter 50 value 6.626386
## iter 60 value 6.505362
## iter 70 value 6.344177
## iter 80 value 5.833663
## iter 90 value 5.743775
## iter 100 value 5.674803
## final value 5.674803
## stopped after 100 iterations
## # weights: 12
## initial value 321.197058
## iter 10 value 37.992700
## iter 20 value 35.792204
## iter 30 value 35.389851
## iter 40 value 35.128294
## iter 50 value 34.925662
## iter 60 value 34.886890
## iter 70 value 34.877191
## iter 80 value 34.875236
## iter 90 value 34.873956
## iter 100 value 34.872360
## final value 34.872360
## stopped after 100 iterations
## # weights: 34
## initial value 389.682359
## iter 10 value 33.677993
## iter 20 value 21.377728
## iter 30 value 14.878733
## iter 40 value 10.094345
## iter 50 value 9.445055
## iter 60 value 9.423292
## iter 70 value 9.419223
## iter 80 value 9.418886
## iter 90 value 9.418811
## iter 100 value 9.418768
## final value 9.418768
## stopped after 100 iterations
## # weights: 56
## initial value 266.773981
## iter 10 value 23.397408
## iter 20 value 12.453893
## iter 30 value 9.921559
## iter 40 value 9.334908
## iter 50 value 9.183171
## iter 60 value 9.172849
## iter 70 value 9.127395
## iter 80 value 9.044680
## iter 90 value 8.967422
## iter 100 value 8.951409
## final value 8.951409
## stopped after 100 iterations
## # weights: 12
## initial value 320.530859
## iter 10 value 60.495799
## iter 20 value 46.020850
## iter 30 value 44.525762
## final value 44.523218
## converged
## # weights: 34
## initial value 407.705422
## iter 10 value 54.162248
## iter 20 value 34.360603
## iter 30 value 34.038423
## iter 40 value 34.035667
## final value 34.035016
## converged
## # weights: 56
## initial value 454.017135
## iter 10 value 69.049091
## iter 20 value 33.996446
## iter 30 value 31.515000
## iter 40 value 31.231004
## iter 50 value 30.903611
## iter 60 value 30.710978
## iter 70 value 30.627870
## iter 80 value 30.624795
## iter 80 value 30.624795
## iter 80 value 30.624795
## final value 30.624795
## converged
## # weights: 12
## initial value 400.796335
## iter 10 value 36.480916
## iter 20 value 32.950616
## iter 30 value 32.894397
## iter 40 value 32.885858
## iter 50 value 32.882953
## iter 60 value 32.875174
## iter 70 value 32.873837
## iter 80 value 32.873014
## iter 90 value 32.870765
## iter 100 value 32.499482
## final value 32.499482
## stopped after 100 iterations
## # weights: 34
## initial value 382.700891
## iter 10 value 35.650965
## iter 20 value 23.665512
## iter 30 value 17.281386
## iter 40 value 10.154502
## iter 50 value 6.686735
## iter 60 value 6.244087
## iter 70 value 6.211796
## iter 80 value 6.196180
## iter 90 value 6.162809
## iter 100 value 6.152142
## final value 6.152142
## stopped after 100 iterations
## # weights: 56
## initial value 297.860371
## iter 10 value 27.305970
## iter 20 value 15.483042
## iter 30 value 9.690338
## iter 40 value 8.129648
## iter 50 value 6.973134
## iter 60 value 6.906713
## iter 70 value 6.869101
## iter 80 value 6.840714
## iter 90 value 6.822533
## iter 100 value 6.807764
## final value 6.807764
## stopped after 100 iterations
## # weights: 12
## initial value 339.456790
## iter 10 value 158.046015
## iter 20 value 65.717796
## iter 30 value 46.787679
## iter 40 value 38.271274
## iter 50 value 36.848776
## iter 60 value 36.575094
## iter 70 value 36.226332
## iter 80 value 36.046096
## iter 90 value 36.039025
## iter 100 value 35.937393
## final value 35.937393
## stopped after 100 iterations
## # weights: 34
## initial value 396.446083
## iter 10 value 49.591717
## iter 20 value 33.545530
## iter 30 value 29.656480
## iter 40 value 27.447190
## iter 50 value 26.319019
## iter 60 value 24.899691
## iter 70 value 24.678813
## iter 80 value 24.516495
## iter 90 value 24.149603
## iter 100 value 23.599254
## final value 23.599254
## stopped after 100 iterations
## # weights: 56
## initial value 325.372656
## iter 10 value 34.301235
## iter 20 value 20.901145
## iter 30 value 13.020419
## iter 40 value 12.256197
## iter 50 value 12.250246
## iter 60 value 12.250019
## iter 70 value 12.249909
## iter 80 value 12.033268
## iter 90 value 11.818461
## iter 100 value 11.811133
## final value 11.811133
## stopped after 100 iterations
## # weights: 12
## initial value 327.906693
## iter 10 value 61.219539
## iter 20 value 49.734517
## iter 30 value 49.118216
## iter 40 value 49.101690
## iter 40 value 49.101690
## iter 40 value 49.101690
## final value 49.101690
## converged
## # weights: 34
## initial value 323.934935
## iter 10 value 46.152240
## iter 20 value 40.840436
## iter 30 value 39.749908
## iter 40 value 39.514367
## iter 50 value 39.426968
## final value 39.426886
## converged
## # weights: 56
## initial value 449.227090
## iter 10 value 81.409576
## iter 20 value 46.841838
## iter 30 value 43.443981
## iter 40 value 41.657814
## iter 50 value 40.991857
## iter 60 value 40.891983
## iter 70 value 40.873833
## iter 80 value 40.872856
## final value 40.872681
## converged
## # weights: 12
## initial value 326.766631
## iter 10 value 44.353548
## iter 20 value 37.431520
## iter 30 value 33.556132
## iter 40 value 30.479939
## iter 50 value 30.467985
## iter 60 value 30.463700
## iter 70 value 30.458655
## iter 80 value 30.458144
## iter 90 value 30.457322
## final value 30.457314
## converged
## # weights: 34
## initial value 428.667035
## iter 10 value 43.686421
## iter 20 value 30.592240
## iter 30 value 26.969605
## iter 40 value 24.266306
## iter 50 value 23.839056
## iter 60 value 22.804244
## iter 70 value 22.668008
## iter 80 value 22.540945
## iter 90 value 22.469185
## iter 100 value 22.427527
## final value 22.427527
## stopped after 100 iterations
## # weights: 56
## initial value 408.874223
## iter 10 value 34.951565
## iter 20 value 12.870980
## iter 30 value 6.483366
## iter 40 value 6.307095
## iter 50 value 6.206916
## iter 60 value 6.150471
## iter 70 value 6.133738
## iter 80 value 6.117790
## iter 90 value 6.088511
## iter 100 value 6.066790
## final value 6.066790
## stopped after 100 iterations
## # weights: 12
## initial value 321.754451
## iter 10 value 42.735297
## iter 20 value 36.553903
## iter 30 value 36.425583
## iter 40 value 36.361319
## iter 50 value 36.346991
## iter 60 value 36.339317
## iter 70 value 36.331902
## iter 80 value 36.324998
## iter 90 value 36.321886
## iter 100 value 36.318710
## final value 36.318710
## stopped after 100 iterations
## # weights: 34
## initial value 300.446391
## iter 10 value 41.961751
## iter 20 value 30.257550
## iter 30 value 28.183516
## iter 40 value 22.689068
## iter 50 value 20.829629
## iter 60 value 19.144823
## iter 70 value 16.434577
## iter 80 value 15.876475
## iter 90 value 15.736186
## iter 100 value 15.618940
## final value 15.618940
## stopped after 100 iterations
## # weights: 56
## initial value 282.021100
## iter 10 value 32.901459
## iter 20 value 16.152079
## iter 30 value 12.074062
## iter 40 value 10.867879
## iter 50 value 10.565675
## iter 60 value 10.464206
## iter 70 value 10.422227
## iter 80 value 10.405932
## iter 90 value 10.394482
## iter 100 value 10.390740
## final value 10.390740
## stopped after 100 iterations
## # weights: 12
## initial value 338.542762
## iter 10 value 58.364417
## iter 20 value 47.889755
## iter 30 value 46.959061
## iter 40 value 46.913157
## final value 46.913156
## converged
## # weights: 34
## initial value 389.067606
## iter 10 value 64.563624
## iter 20 value 47.862307
## iter 30 value 41.700191
## iter 40 value 38.501372
## iter 50 value 37.391377
## iter 60 value 37.049787
## iter 70 value 36.855211
## iter 80 value 36.830464
## iter 90 value 36.830243
## final value 36.830243
## converged
## # weights: 56
## initial value 314.006605
## iter 10 value 39.786364
## iter 20 value 36.825762
## iter 30 value 36.042459
## iter 40 value 35.790063
## iter 50 value 35.749193
## iter 60 value 35.630582
## iter 70 value 34.800295
## iter 80 value 34.775821
## final value 34.775630
## converged
## # weights: 12
## initial value 317.842554
## iter 10 value 55.219614
## iter 20 value 42.411436
## iter 30 value 41.698575
## iter 40 value 36.533028
## iter 50 value 36.474701
## iter 60 value 36.466201
## iter 70 value 36.462348
## iter 80 value 36.458830
## iter 90 value 36.457076
## iter 100 value 36.456465
## final value 36.456465
## stopped after 100 iterations
## # weights: 34
## initial value 315.805100
## iter 10 value 35.696766
## iter 20 value 25.008868
## iter 30 value 19.314407
## iter 40 value 18.470075
## iter 50 value 18.313588
## iter 60 value 18.124856
## iter 70 value 17.847942
## iter 80 value 17.804029
## iter 90 value 17.675273
## iter 100 value 17.511005
## final value 17.511005
## stopped after 100 iterations
## # weights: 56
## initial value 336.033457
## iter 10 value 29.843576
## iter 20 value 19.062788
## iter 30 value 12.266330
## iter 40 value 9.990162
## iter 50 value 9.510100
## iter 60 value 7.360044
## iter 70 value 5.808873
## iter 80 value 5.238995
## iter 90 value 5.006630
## iter 100 value 3.884940
## final value 3.884940
## stopped after 100 iterations
## # weights: 12
## initial value 373.448951
## iter 10 value 45.158611
## iter 20 value 44.096037
## iter 30 value 43.343767
## iter 40 value 42.568253
## iter 50 value 42.525430
## final value 42.525369
## converged
## # weights: 34
## initial value 303.311657
## iter 10 value 39.258404
## iter 20 value 35.907574
## iter 30 value 32.926632
## iter 40 value 31.719819
## iter 50 value 31.054626
## iter 60 value 30.293635
## iter 70 value 28.886476
## iter 80 value 27.435616
## iter 90 value 26.566727
## iter 100 value 26.357336
## final value 26.357336
## stopped after 100 iterations
## # weights: 56
## initial value 297.717687
## iter 10 value 34.910317
## iter 20 value 19.097253
## iter 30 value 12.162892
## iter 40 value 11.962253
## iter 50 value 11.882074
## iter 60 value 11.825032
## iter 70 value 11.803696
## iter 80 value 11.799958
## iter 90 value 11.789507
## iter 100 value 11.633687
## final value 11.633687
## stopped after 100 iterations
## # weights: 12
## initial value 323.344070
## iter 10 value 59.196471
## iter 20 value 54.572127
## iter 30 value 54.313230
## final value 54.265527
## converged
## # weights: 34
## initial value 422.110821
## iter 10 value 46.135214
## iter 20 value 42.100740
## iter 30 value 41.349736
## iter 40 value 40.946203
## iter 50 value 40.871524
## iter 60 value 40.864045
## iter 70 value 40.862853
## iter 80 value 40.859037
## iter 90 value 40.856820
## final value 40.856812
## converged
## # weights: 56
## initial value 306.653715
## iter 10 value 61.734533
## iter 20 value 46.398463
## iter 30 value 42.976429
## iter 40 value 40.535781
## iter 50 value 38.903458
## iter 60 value 38.436156
## iter 70 value 38.371343
## iter 80 value 37.583306
## iter 90 value 37.167963
## iter 100 value 37.139218
## final value 37.139218
## stopped after 100 iterations
## # weights: 12
## initial value 302.848084
## iter 10 value 86.222643
## iter 20 value 62.549374
## iter 30 value 50.117327
## iter 40 value 42.790245
## iter 50 value 42.746444
## iter 60 value 42.704572
## iter 70 value 42.700133
## iter 80 value 42.697799
## iter 90 value 42.695362
## iter 100 value 42.693661
## final value 42.693661
## stopped after 100 iterations
## # weights: 34
## initial value 357.848719
## iter 10 value 50.353275
## iter 20 value 33.751623
## iter 30 value 32.033219
## iter 40 value 29.597684
## iter 50 value 28.331533
## iter 60 value 28.273034
## iter 70 value 28.207846
## iter 80 value 28.122741
## iter 90 value 28.036842
## iter 100 value 27.937599
## final value 27.937599
## stopped after 100 iterations
## # weights: 56
## initial value 401.661057
## iter 10 value 36.655848
## iter 20 value 16.011617
## iter 30 value 6.895899
## iter 40 value 6.428734
## iter 50 value 6.185441
## iter 60 value 6.111802
## iter 70 value 6.021786
## iter 80 value 5.957063
## iter 90 value 4.807588
## iter 100 value 4.566685
## final value 4.566685
## stopped after 100 iterations
## # weights: 12
## initial value 341.667320
## iter 10 value 50.797393
## iter 20 value 39.826114
## iter 30 value 39.597097
## iter 40 value 39.548037
## iter 50 value 39.525868
## iter 60 value 39.514783
## iter 70 value 39.503536
## iter 80 value 39.500868
## iter 90 value 39.494332
## iter 100 value 39.491669
## final value 39.491669
## stopped after 100 iterations
## # weights: 34
## initial value 330.883225
## iter 10 value 33.931619
## iter 20 value 20.963873
## iter 30 value 14.558637
## iter 40 value 13.880806
## iter 50 value 13.768648
## iter 60 value 13.766768
## iter 70 value 13.766501
## iter 80 value 13.766430
## final value 13.766410
## converged
## # weights: 56
## initial value 366.759875
## iter 10 value 31.998240
## iter 20 value 19.326931
## iter 30 value 13.284252
## iter 40 value 13.139520
## iter 50 value 13.128890
## final value 13.128861
## converged
## # weights: 12
## initial value 360.723364
## iter 10 value 79.764384
## iter 20 value 58.554909
## iter 30 value 51.915182
## iter 40 value 48.699427
## final value 48.688574
## converged
## # weights: 34
## initial value 421.298943
## iter 10 value 85.137440
## iter 20 value 41.359032
## iter 30 value 38.548152
## iter 40 value 36.875606
## iter 50 value 36.830442
## final value 36.830432
## converged
## # weights: 56
## initial value 304.019070
## iter 10 value 43.255238
## iter 20 value 38.254552
## iter 30 value 36.850424
## iter 40 value 36.648594
## iter 50 value 36.550726
## iter 60 value 36.327745
## iter 70 value 36.271464
## iter 80 value 36.254873
## iter 90 value 36.253762
## final value 36.253760
## converged
## # weights: 12
## initial value 328.582102
## iter 10 value 46.350500
## iter 20 value 40.087911
## iter 30 value 39.780897
## iter 40 value 39.642761
## iter 50 value 39.626044
## iter 60 value 39.620422
## iter 70 value 39.617573
## iter 80 value 39.616729
## iter 90 value 39.616403
## iter 100 value 39.616162
## final value 39.616162
## stopped after 100 iterations
## # weights: 34
## initial value 420.985148
## iter 10 value 40.782041
## iter 20 value 31.436978
## iter 30 value 29.810226
## iter 40 value 29.752897
## iter 50 value 29.731764
## iter 60 value 29.722300
## iter 70 value 29.708030
## iter 80 value 29.701166
## iter 90 value 29.695962
## iter 100 value 29.687023
## final value 29.687023
## stopped after 100 iterations
## # weights: 56
## initial value 305.807773
## iter 10 value 32.868515
## iter 20 value 28.672268
## iter 30 value 23.994374
## iter 40 value 22.396665
## iter 50 value 22.173441
## iter 60 value 22.095568
## iter 70 value 21.691679
## iter 80 value 20.198882
## iter 90 value 19.546455
## iter 100 value 19.371765
## final value 19.371765
## stopped after 100 iterations
## # weights: 34
## initial value 326.919377
## iter 10 value 36.157512
## iter 20 value 30.097667
## iter 30 value 23.574816
## iter 40 value 23.268963
## iter 50 value 23.077785
## iter 60 value 20.578788
## iter 70 value 19.367780
## iter 80 value 19.204978
## iter 90 value 18.867884
## iter 100 value 18.771249
## final value 18.771249
## stopped after 100 iterations
<- predict(modelo5, entrenamiento)
resultado_entrenamiento5
<- predict(modelo5, prueba) resultado_prueba5
<- confusionMatrix(resultado_entrenamiento5, entrenamiento$Class)
mcre5
mcre5
## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 351 0
## malignant 5 192
##
## Accuracy : 0.9909
## 95% CI : (0.9788, 0.997)
## No Information Rate : 0.6496
## P-Value [Acc > NIR] : < 2e-16
##
## Kappa : 0.9801
##
## Mcnemar's Test P-Value : 0.07364
##
## Sensitivity : 0.9860
## Specificity : 1.0000
## Pos Pred Value : 1.0000
## Neg Pred Value : 0.9746
## Prevalence : 0.6496
## Detection Rate : 0.6405
## Detection Prevalence : 0.6405
## Balanced Accuracy : 0.9930
##
## 'Positive' Class : benign
##
<- confusionMatrix(resultado_prueba5, prueba$Class)
mcrp5
mcrp5
## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 86 4
## malignant 2 43
##
## Accuracy : 0.9556
## 95% CI : (0.9058, 0.9835)
## No Information Rate : 0.6519
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.9011
##
## Mcnemar's Test P-Value : 0.6831
##
## Sensitivity : 0.9773
## Specificity : 0.9149
## Pos Pred Value : 0.9556
## Neg Pred Value : 0.9556
## Prevalence : 0.6519
## Detection Rate : 0.6370
## Detection Prevalence : 0.6667
## Balanced Accuracy : 0.9461
##
## 'Positive' Class : benign
##
<- train(Class ~ ., data = entrenamiento, method = "rf", preProcess = c("scale", "center"), trControl = trainControl(method="cv", number = 10), tuneGrid = expand.grid(mtry =c(2,4,6)))
modelo6
<- predict(modelo6, entrenamiento)
resultado_entrenamiento6 <- predict(modelo6, prueba) resultado_prueba6
<- confusionMatrix(resultado_entrenamiento6, entrenamiento$Class)
mcre6 mcre6
## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 356 0
## malignant 0 192
##
## Accuracy : 1
## 95% CI : (0.9933, 1)
## No Information Rate : 0.6496
## 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 : 0.6496
## Detection Rate : 0.6496
## Detection Prevalence : 0.6496
## Balanced Accuracy : 1.0000
##
## 'Positive' Class : benign
##
<- confusionMatrix(resultado_prueba6, prueba$Class)
mcrp6 mcrp6
## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 85 1
## malignant 3 46
##
## Accuracy : 0.9704
## 95% CI : (0.9259, 0.9919)
## No Information Rate : 0.6519
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.9354
##
## Mcnemar's Test P-Value : 0.6171
##
## Sensitivity : 0.9659
## Specificity : 0.9787
## Pos Pred Value : 0.9884
## Neg Pred Value : 0.9388
## Prevalence : 0.6519
## Detection Rate : 0.6296
## Detection Prevalence : 0.6370
## Balanced Accuracy : 0.9723
##
## 'Positive' Class : benign
##
<- data.frame(
resumen
"svmLinear" = c(mcre1$overall["Accuracy"], mcre1$overall["Accuracy"]),
"svmRadial" = c(mcre2$overall["Accuracy"], mcrp2$overall["Accuracy"]),
"svmPoly" = c(mcre3$overall["Accuracy"], mcrp3$overall["Accuracy"]),
"rpart" = c(mcre4$overall["Accuracy"], mcrp4$overall["Accuracy"]),
"NeuralNet" = c(mcre5$overall["Accuracy"], mcrp5$overall["Accuracy"]),
"RandomForest" = c(mcre6$overall["Accuracy"], mcrp6$overall["Accuracy"])
)
rownames(resumen) <- c("Precision de entrenamiento", "Precision de prueba")
resumen
## svmLinear svmRadial svmPoly rpart NeuralNet
## Precision de entrenamiento 0.9708029 0.9963504 0.9708029 0.9635036 0.9908759
## Precision de prueba 0.9708029 0.9555556 0.9777778 0.9555556 0.9555556
## RandomForest
## Precision de entrenamiento 1.0000000
## Precision de prueba 0.9703704
El modelo que utiliza la técnica de redes neuronales muestra claros signos de sobreajuste. Este fenómeno se hace evidente al observar que el modelo alcanza niveles de precisión excepcionalmente altos durante la etapa de entrenamiento, pero dicha precisión experimenta una notable reducción cuando el modelo es sometido a pruebas con datos nuevos. Este comportamiento indica que, aunque el modelo es muy eficaz para predecir resultados dentro del conjunto de datos con el que fue entrenado, su capacidad para generalizar y predecir con precisión sobre conjuntos de datos no vistos es limitada.
De acuerdo con el análisis detallado de los resultados obtenidos de diferentes modelos, se ha determinado que los modelos basados en Máquina de Vectores de Soporte Lineal y Máquina de Vectores de Soporte con Kernel Polinómico (Máquina de Vectores Poly) destacan sobre el resto. Estos modelos no solo han demostrado tener una capacidad superior para mantener una alta precisión tanto en las fases de entrenamiento como de prueba, sino que también han evitado el problema del sobreajuste. Este equilibrio entre la precisión en el entrenamiento y en la prueba sugiere que ambos modelos tienen una robusta capacidad de generalización, haciéndolos adecuados para predecir resultados de manera confiable en datos nuevos o desconocidos. La eficacia de estos modelos en mantener la consistencia de su rendimiento a través de diferentes conjuntos de datos resalta su valor y los posiciona como las opciones preferentes para tareas de predicción en este contexto.