#install.packages(“mlbench”)
library(mlbench)
library(caret)## Loading required package: ggplot2## Loading required package: latticedata(BreastCancer)#Incluir funciones para evaluación y comparación de modelos: resamples() dotplot()
df <- BreastCancer
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 ...#create_report(df)df <- df[, -which(names(df) == "Id")]
df <- na.omit(df) # Eliminar filas con valores NA
df$Cl.thickness <- as.numeric(as.character(df$Cl.thickness))
df$Cell.size <- as.numeric(as.character(df$Cell.size))
df$Cell.shape <- as.numeric(as.character(df$Cell.shape))
df$Marg.adhesion <- as.numeric(as.character(df$Marg.adhesion))
df$Epith.c.size <- as.numeric(as.character(df$Epith.c.size))
df$Bare.nuclei <- as.numeric(as.character(df$Bare.nuclei))
df$Bl.cromatin <- as.numeric(as.character(df$Bl.cromatin))
df$Normal.nucleoli <- as.numeric(as.character(df$Normal.nucleoli))
df$Mitoses <- as.numeric(as.character(df$Mitoses))set.seed(123)
renglones_entrenamiento <- createDataPartition(df$Class, p=0.8, list=FALSE)
entrenamiento <- df[renglones_entrenamiento, ]
prueba <- df[-renglones_entrenamiento, ]
modelo1 <- train(Class ~ ., data=entrenamiento, method = "svmLinear", preProcess=c("scale","center"), trControl = trainControl(method="cv", number=10), tuneGrind = data.frame(C=1)# cuando es svmLinear
)
resultado_entrenamiento1 <- predict(modelo1,entrenamiento)
resultado_prueba1 <- predict(modelo1,prueba)
# Matriz de Confusión
mcre1 <- confusionMatrix(resultado_entrenamiento1, entrenamiento$Class) # matriz de confusión del resultado del entrenamiento
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
## mcrp1 <- confusionMatrix(resultado_prueba1, prueba$Class) # matriz de confusión del resultado de la prueba
mcrp1## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 87 2
## malignant 1 45
##
## Accuracy : 0.9778
## 95% CI : (0.9364, 0.9954)
## No Information Rate : 0.6519
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.9508
##
## Mcnemar's Test P-Value : 1
##
## Sensitivity : 0.9886
## Specificity : 0.9574
## Pos Pred Value : 0.9775
## Neg Pred Value : 0.9783
## Prevalence : 0.6519
## Detection Rate : 0.6444
## Detection Prevalence : 0.6593
## Balanced Accuracy : 0.9730
##
## 'Positive' Class : benign
## modelo2 <- train(Class ~ ., data=entrenamiento, method = "svmRadial", preProcess=c("scale","center"), trControl = trainControl(method="cv", number=10), tuneGrind = data.frame(sigma=1, C=1)# cuando es svmradial
)
resultado_entrenamiento2 <- predict(modelo2,entrenamiento)
resultado_prueba2 <- predict(modelo2,prueba)
# Matriz de Confusión
mcre2 <- confusionMatrix(resultado_entrenamiento2, entrenamiento$Class) # matriz de confusión del resultado del entrenamiento
mcre2## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 349 1
## malignant 7 191
##
## Accuracy : 0.9854
## 95% CI : (0.9714, 0.9937)
## No Information Rate : 0.6496
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.9682
##
## Mcnemar's Test P-Value : 0.0771
##
## Sensitivity : 0.9803
## Specificity : 0.9948
## Pos Pred Value : 0.9971
## Neg Pred Value : 0.9646
## Prevalence : 0.6496
## Detection Rate : 0.6369
## Detection Prevalence : 0.6387
## Balanced Accuracy : 0.9876
##
## 'Positive' Class : benign
## mcrp2 <- confusionMatrix(resultado_prueba2, prueba$Class) # matriz de confusión del resultado de la prueba
mcrp2## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 83 0
## malignant 5 47
##
## Accuracy : 0.963
## 95% CI : (0.9157, 0.9879)
## No Information Rate : 0.6519
## P-Value [Acc > NIR] : < 2e-16
##
## Kappa : 0.9204
##
## Mcnemar's Test P-Value : 0.07364
##
## Sensitivity : 0.9432
## Specificity : 1.0000
## Pos Pred Value : 1.0000
## Neg Pred Value : 0.9038
## Prevalence : 0.6519
## Detection Rate : 0.6148
## Detection Prevalence : 0.6148
## Balanced Accuracy : 0.9716
##
## 'Positive' Class : benign
## modelo3 <- train(Class ~ ., data=entrenamiento, method = "svmPoly", preProcess=c("scale","center"), trControl = trainControl(method="cv", number=10), tuneGrind = data.frame(degree=1,scale=1, C=1)# cuando es svmPoly
)
resultado_entrenamiento3 <- predict(modelo3,entrenamiento)
resultado_prueba3 <- predict(modelo3,prueba)
# Matriz de Confusión
mcre3 <- confusionMatrix(resultado_entrenamiento3, entrenamiento$Class) # matriz de confusión del resultado del entrenamiento
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
## mcrp3 <- confusionMatrix(resultado_prueba3, prueba$Class) # matriz de confusión del resultado de la prueba
mcrp3## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 86 2
## malignant 2 45
##
## Accuracy : 0.9704
## 95% CI : (0.9259, 0.9919)
## No Information Rate : 0.6519
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.9347
##
## Mcnemar's Test P-Value : 1
##
## Sensitivity : 0.9773
## Specificity : 0.9574
## Pos Pred Value : 0.9773
## Neg Pred Value : 0.9574
## Prevalence : 0.6519
## Detection Rate : 0.6370
## Detection Prevalence : 0.6519
## Balanced Accuracy : 0.9674
##
## 'Positive' Class : benign
## modelo4 <- train(Class ~ ., data=entrenamiento, method = "rpart", preProcess=c("scale","center"), trControl = trainControl(method="cv", number=10), tuneLength = 5# cuando es rpart
)
resultado_entrenamiento4 <- predict(modelo4,entrenamiento)
resultado_prueba4 <- predict(modelo4,prueba)
# Matriz de Confusión
mcre4 <- confusionMatrix(resultado_entrenamiento4, entrenamiento$Class) # matriz de confusión del resultado del entrenamiento
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
## mcrp4 <- confusionMatrix(resultado_prueba4, prueba$Class) # matriz de confusión del resultado de la prueba
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
## modelo5 <- train(Class ~ ., data=entrenamiento,
method = "nnet",
preProcess=c("scale","center"),
trControl = trainControl(method="cv", number=10)
# cuando es nnet
)## # weights: 12
## initial value 343.094303
## iter 10 value 56.636764
## iter 20 value 53.058746
## iter 30 value 50.734511
## iter 40 value 49.946980
## iter 50 value 48.467939
## iter 60 value 48.443675
## iter 70 value 48.430363
## iter 80 value 48.429295
## iter 90 value 48.428800
## iter 100 value 48.427369
## final value 48.427369
## stopped after 100 iterations
## # weights: 34
## initial value 356.826640
## iter 10 value 32.074068
## iter 20 value 23.691591
## iter 30 value 17.857097
## iter 40 value 16.722242
## iter 50 value 16.532574
## iter 60 value 16.468285
## iter 70 value 16.465880
## iter 80 value 16.454306
## iter 90 value 16.430481
## iter 100 value 16.428145
## final value 16.428145
## stopped after 100 iterations
## # weights: 56
## initial value 544.853171
## iter 10 value 29.135965
## iter 20 value 20.054533
## iter 30 value 9.975023
## iter 40 value 7.680456
## iter 50 value 6.782733
## iter 60 value 6.232243
## iter 70 value 5.774634
## iter 80 value 4.800660
## iter 90 value 4.781704
## iter 100 value 4.781514
## final value 4.781514
## stopped after 100 iterations
## # weights: 12
## initial value 307.283886
## iter 10 value 55.748205
## iter 20 value 51.347028
## iter 30 value 50.946970
## final value 50.946957
## converged
## # weights: 34
## initial value 319.882055
## iter 10 value 68.911231
## iter 20 value 47.269852
## iter 30 value 39.983767
## iter 40 value 37.976355
## iter 50 value 37.371554
## iter 60 value 37.315749
## iter 70 value 37.204730
## iter 80 value 37.201535
## iter 80 value 37.201535
## iter 80 value 37.201535
## final value 37.201535
## converged
## # weights: 56
## initial value 343.805285
## iter 10 value 69.789182
## iter 20 value 40.273600
## iter 30 value 38.546679
## iter 40 value 37.777575
## iter 50 value 37.041389
## iter 60 value 37.014437
## iter 70 value 36.890627
## iter 80 value 36.804635
## iter 90 value 36.548635
## iter 100 value 36.541121
## final value 36.541121
## stopped after 100 iterations
## # weights: 12
## initial value 331.189030
## iter 10 value 44.828119
## iter 20 value 40.543889
## iter 30 value 40.233697
## iter 40 value 39.650642
## iter 50 value 39.637448
## final value 39.637430
## converged
## # weights: 34
## initial value 482.495648
## iter 10 value 38.269235
## iter 20 value 28.263330
## iter 30 value 22.386448
## iter 40 value 20.361023
## iter 50 value 18.693814
## iter 60 value 17.194339
## iter 70 value 16.783430
## iter 80 value 16.578976
## iter 90 value 16.541276
## iter 100 value 16.516258
## final value 16.516258
## stopped after 100 iterations
## # weights: 56
## initial value 353.185438
## iter 10 value 40.894842
## iter 20 value 32.863336
## iter 30 value 31.433372
## iter 40 value 31.214406
## iter 50 value 30.738503
## iter 60 value 30.026135
## iter 70 value 29.752462
## iter 80 value 29.598979
## iter 90 value 29.120300
## iter 100 value 28.875687
## final value 28.875687
## stopped after 100 iterations
## # weights: 12
## initial value 301.863311
## iter 10 value 43.571929
## iter 20 value 42.884141
## iter 30 value 42.564806
## iter 40 value 42.550094
## iter 50 value 42.541121
## iter 60 value 42.537610
## iter 70 value 42.535236
## iter 80 value 42.532932
## iter 90 value 42.532139
## iter 100 value 42.530604
## final value 42.530604
## stopped after 100 iterations
## # weights: 34
## initial value 340.507299
## iter 10 value 42.930729
## iter 20 value 41.979862
## iter 30 value 40.759776
## iter 40 value 39.711064
## iter 50 value 36.875053
## iter 60 value 35.652621
## iter 70 value 34.661923
## iter 80 value 32.635186
## iter 90 value 25.648925
## iter 100 value 21.103093
## final value 21.103093
## stopped after 100 iterations
## # weights: 56
## initial value 297.095553
## iter 10 value 33.302208
## iter 20 value 19.121222
## iter 30 value 13.706333
## iter 40 value 11.451945
## iter 50 value 10.955334
## iter 60 value 10.897900
## iter 70 value 10.805012
## iter 80 value 10.797258
## iter 90 value 10.773625
## iter 100 value 10.548413
## final value 10.548413
## stopped after 100 iterations
## # weights: 12
## initial value 360.246313
## iter 10 value 66.925960
## iter 20 value 54.021793
## iter 30 value 53.448151
## iter 40 value 53.270000
## final value 53.269978
## converged
## # weights: 34
## initial value 299.023143
## iter 10 value 66.440903
## iter 20 value 45.264453
## iter 30 value 43.480582
## iter 40 value 43.462397
## iter 50 value 42.765503
## iter 60 value 41.865365
## iter 70 value 40.985002
## iter 80 value 40.969761
## final value 40.969745
## converged
## # weights: 56
## initial value 386.343749
## iter 10 value 48.162365
## iter 20 value 42.844680
## iter 30 value 41.212715
## iter 40 value 40.327318
## iter 50 value 38.887572
## iter 60 value 38.120384
## iter 70 value 38.005933
## final value 38.005592
## converged
## # weights: 12
## initial value 308.882984
## iter 10 value 39.956091
## iter 20 value 39.744202
## iter 30 value 36.657642
## iter 40 value 34.709321
## iter 50 value 34.623596
## iter 60 value 34.465560
## iter 70 value 34.346015
## iter 80 value 34.302765
## iter 90 value 34.263923
## iter 100 value 34.176733
## final value 34.176733
## stopped after 100 iterations
## # weights: 34
## initial value 285.451531
## iter 10 value 33.486790
## iter 20 value 27.231813
## iter 30 value 21.342308
## iter 40 value 19.042263
## iter 50 value 18.681800
## iter 60 value 18.610544
## iter 70 value 18.481554
## iter 80 value 18.334381
## iter 90 value 18.253581
## iter 100 value 18.223811
## final value 18.223811
## stopped after 100 iterations
## # weights: 56
## initial value 359.077155
## iter 10 value 33.147667
## iter 20 value 18.871371
## iter 30 value 11.599025
## iter 40 value 9.161732
## iter 50 value 8.354897
## iter 60 value 7.531120
## iter 70 value 7.381134
## iter 80 value 6.922982
## iter 90 value 6.356298
## iter 100 value 5.286015
## final value 5.286015
## stopped after 100 iterations
## # weights: 12
## initial value 326.846865
## iter 10 value 38.750173
## iter 20 value 36.259168
## iter 30 value 36.019306
## iter 40 value 34.237884
## iter 50 value 32.782650
## iter 60 value 32.776709
## iter 70 value 32.776385
## iter 80 value 32.775866
## iter 90 value 32.775774
## iter 100 value 32.775201
## final value 32.775201
## stopped after 100 iterations
## # weights: 34
## initial value 316.248078
## iter 10 value 36.945896
## iter 20 value 30.641599
## iter 30 value 28.659440
## iter 40 value 27.525186
## iter 50 value 25.638706
## iter 60 value 25.048531
## iter 70 value 24.807797
## iter 80 value 24.778579
## iter 90 value 24.773586
## iter 100 value 24.769883
## final value 24.769883
## stopped after 100 iterations
## # weights: 56
## initial value 433.459424
## iter 10 value 32.677747
## iter 20 value 22.087315
## iter 30 value 14.735582
## iter 40 value 8.479961
## iter 50 value 7.929549
## iter 60 value 7.923285
## iter 70 value 7.922234
## iter 80 value 7.921992
## final value 7.921987
## converged
## # weights: 12
## initial value 331.041915
## iter 10 value 58.491470
## iter 20 value 50.605704
## iter 30 value 50.457788
## final value 50.457717
## converged
## # weights: 34
## initial value 418.113818
## iter 10 value 45.290825
## iter 20 value 40.475041
## iter 30 value 40.214205
## iter 40 value 40.179842
## iter 50 value 40.170993
## final value 40.170975
## converged
## # weights: 56
## initial value 427.123502
## iter 10 value 118.105834
## iter 20 value 44.914641
## iter 30 value 40.218004
## iter 40 value 39.844956
## iter 50 value 39.764696
## iter 60 value 39.642973
## iter 70 value 39.641156
## iter 80 value 39.639902
## iter 90 value 39.545617
## iter 100 value 39.216055
## final value 39.216055
## stopped after 100 iterations
## # weights: 12
## initial value 347.426887
## iter 10 value 49.815956
## iter 20 value 42.887906
## iter 30 value 42.726491
## iter 40 value 42.683871
## iter 50 value 42.678635
## iter 60 value 42.677607
## iter 70 value 42.677283
## iter 80 value 42.676975
## iter 90 value 42.676808
## iter 100 value 42.676753
## final value 42.676753
## stopped after 100 iterations
## # weights: 34
## initial value 307.100009
## iter 10 value 34.710388
## iter 20 value 26.152417
## iter 30 value 20.430756
## iter 40 value 18.944733
## iter 50 value 17.915280
## iter 60 value 17.725616
## iter 70 value 17.624010
## iter 80 value 17.565745
## iter 90 value 17.493842
## iter 100 value 17.415927
## final value 17.415927
## stopped after 100 iterations
## # weights: 56
## initial value 349.579451
## iter 10 value 33.308444
## iter 20 value 18.953648
## iter 30 value 12.600243
## iter 40 value 11.567857
## iter 50 value 10.723014
## iter 60 value 9.817809
## iter 70 value 9.709208
## iter 80 value 9.603886
## iter 90 value 9.496832
## iter 100 value 9.381030
## final value 9.381030
## stopped after 100 iterations
## # weights: 12
## initial value 359.248019
## iter 10 value 61.649279
## iter 20 value 44.567652
## iter 30 value 39.731189
## iter 40 value 39.596761
## iter 50 value 39.509097
## iter 60 value 39.501253
## iter 70 value 39.491334
## iter 80 value 39.487710
## iter 90 value 39.486212
## iter 100 value 39.485235
## final value 39.485235
## stopped after 100 iterations
## # weights: 34
## initial value 346.203030
## iter 10 value 38.893342
## iter 20 value 30.671753
## iter 30 value 29.183896
## iter 40 value 27.329685
## iter 50 value 26.625875
## iter 60 value 26.424134
## iter 70 value 26.369224
## iter 80 value 26.335878
## iter 90 value 26.278457
## iter 100 value 26.237364
## final value 26.237364
## stopped after 100 iterations
## # weights: 56
## initial value 318.878464
## iter 10 value 33.475469
## iter 20 value 14.949675
## iter 30 value 11.870655
## iter 40 value 11.362993
## iter 50 value 11.202148
## iter 60 value 11.174182
## iter 70 value 11.172818
## iter 80 value 11.171722
## iter 90 value 11.170403
## iter 100 value 11.170192
## final value 11.170192
## stopped after 100 iterations
## # weights: 12
## initial value 456.055575
## iter 10 value 61.888667
## iter 20 value 53.598955
## iter 30 value 50.705412
## iter 40 value 50.363829
## final value 50.363821
## converged
## # weights: 34
## initial value 307.606375
## iter 10 value 53.656759
## iter 20 value 43.308981
## iter 30 value 42.274709
## iter 40 value 42.064556
## iter 50 value 41.965387
## final value 41.964661
## converged
## # weights: 56
## initial value 389.875433
## iter 10 value 57.414101
## iter 20 value 45.681726
## iter 30 value 42.216736
## iter 40 value 40.594840
## iter 50 value 39.621367
## iter 60 value 38.465474
## iter 70 value 38.161543
## iter 80 value 38.157623
## iter 90 value 38.157297
## iter 90 value 38.157297
## iter 90 value 38.157297
## final value 38.157297
## converged
## # weights: 12
## initial value 402.025407
## iter 10 value 44.827913
## iter 20 value 39.439357
## iter 30 value 37.914773
## iter 40 value 37.783044
## iter 50 value 37.475684
## iter 60 value 37.404140
## iter 70 value 37.404059
## final value 37.402855
## converged
## # weights: 34
## initial value 441.253553
## iter 10 value 36.184558
## iter 20 value 25.413131
## iter 30 value 22.782597
## iter 40 value 22.340736
## iter 50 value 22.020851
## iter 60 value 21.731710
## iter 70 value 21.640483
## iter 80 value 21.497367
## iter 90 value 21.425293
## iter 100 value 21.044996
## final value 21.044996
## stopped after 100 iterations
## # weights: 56
## initial value 630.982760
## iter 10 value 34.007858
## iter 20 value 25.742977
## iter 30 value 20.044865
## iter 40 value 17.537317
## iter 50 value 16.150256
## iter 60 value 15.003117
## iter 70 value 13.981581
## iter 80 value 13.508862
## iter 90 value 12.247657
## iter 100 value 11.937393
## final value 11.937393
## stopped after 100 iterations
## # weights: 12
## initial value 364.801283
## iter 10 value 46.234030
## iter 20 value 44.824368
## iter 30 value 44.767357
## iter 40 value 44.759174
## iter 50 value 44.752652
## iter 60 value 44.749017
## iter 70 value 44.747941
## iter 80 value 44.746737
## iter 90 value 44.746056
## iter 100 value 44.745096
## final value 44.745096
## stopped after 100 iterations
## # weights: 34
## initial value 327.067693
## iter 10 value 34.361113
## iter 20 value 27.203767
## iter 30 value 21.179010
## iter 40 value 20.994138
## iter 50 value 20.992999
## iter 60 value 20.992897
## iter 70 value 20.991701
## iter 80 value 20.952058
## iter 90 value 20.948934
## iter 100 value 20.912330
## final value 20.912330
## stopped after 100 iterations
## # weights: 56
## initial value 353.170081
## iter 10 value 41.962251
## iter 20 value 28.029577
## iter 30 value 23.067178
## iter 40 value 22.304488
## iter 50 value 21.716763
## iter 60 value 21.560507
## iter 70 value 21.211476
## iter 80 value 21.189275
## iter 90 value 21.047545
## iter 100 value 21.016052
## final value 21.016052
## stopped after 100 iterations
## # weights: 12
## initial value 381.910600
## iter 10 value 66.528958
## iter 20 value 51.327971
## iter 30 value 49.402082
## final value 49.330136
## converged
## # weights: 34
## initial value 368.814453
## iter 10 value 57.425839
## iter 20 value 45.442354
## iter 30 value 43.535140
## iter 40 value 41.392579
## iter 50 value 40.160522
## iter 60 value 39.730478
## iter 70 value 39.709498
## iter 80 value 39.708278
## final value 39.708254
## converged
## # weights: 56
## initial value 328.618259
## iter 10 value 44.617366
## iter 20 value 39.682079
## iter 30 value 38.083137
## iter 40 value 37.999349
## iter 50 value 37.913467
## iter 60 value 37.782631
## iter 70 value 37.771137
## final value 37.771135
## converged
## # weights: 12
## initial value 429.657918
## iter 10 value 41.472256
## iter 20 value 38.021511
## iter 30 value 37.360628
## iter 40 value 36.556770
## iter 50 value 36.143158
## iter 60 value 36.119717
## iter 70 value 36.065245
## final value 36.051586
## converged
## # weights: 34
## initial value 303.901236
## iter 10 value 49.960832
## iter 20 value 36.242893
## iter 30 value 33.207508
## iter 40 value 32.468698
## iter 50 value 32.182265
## iter 60 value 32.153825
## iter 70 value 32.109408
## iter 80 value 32.065553
## iter 90 value 32.019585
## iter 100 value 31.702077
## final value 31.702077
## stopped after 100 iterations
## # weights: 56
## initial value 385.286485
## iter 10 value 33.781321
## iter 20 value 20.307803
## iter 30 value 9.044203
## iter 40 value 7.808731
## iter 50 value 7.390546
## iter 60 value 7.137659
## iter 70 value 7.045746
## iter 80 value 7.011040
## iter 90 value 6.973686
## iter 100 value 6.934846
## final value 6.934846
## stopped after 100 iterations
## # weights: 12
## initial value 315.629939
## iter 10 value 46.801101
## iter 20 value 39.505730
## iter 30 value 37.625268
## iter 40 value 36.394424
## iter 50 value 36.327476
## iter 60 value 36.275653
## iter 70 value 36.265881
## iter 80 value 36.263940
## iter 90 value 36.263518
## iter 100 value 36.262894
## final value 36.262894
## stopped after 100 iterations
## # weights: 34
## initial value 295.238119
## iter 10 value 30.999829
## iter 20 value 25.376984
## iter 30 value 22.500262
## iter 40 value 21.826151
## iter 50 value 21.130971
## iter 60 value 20.560892
## iter 70 value 20.496442
## iter 80 value 20.486008
## iter 90 value 20.470252
## iter 100 value 20.461878
## final value 20.461878
## stopped after 100 iterations
## # weights: 56
## initial value 321.267520
## iter 10 value 28.957297
## iter 20 value 23.180012
## iter 30 value 20.644464
## iter 40 value 15.524506
## iter 50 value 12.569357
## iter 60 value 11.935946
## iter 70 value 8.093305
## iter 80 value 6.041206
## iter 90 value 3.149767
## iter 100 value 1.942067
## final value 1.942067
## stopped after 100 iterations
## # weights: 12
## initial value 319.019742
## iter 10 value 77.101553
## iter 20 value 50.928250
## iter 30 value 42.143735
## final value 42.121119
## converged
## # weights: 34
## initial value 330.819317
## iter 10 value 35.472343
## iter 20 value 33.466693
## iter 30 value 32.893997
## iter 40 value 32.851790
## final value 32.849190
## converged
## # weights: 56
## initial value 380.397707
## iter 10 value 38.363698
## iter 20 value 34.107344
## iter 30 value 32.396400
## iter 40 value 31.727546
## iter 50 value 31.419245
## iter 60 value 31.403024
## iter 70 value 31.400077
## iter 80 value 31.398889
## final value 31.398880
## converged
## # weights: 12
## initial value 341.791031
## iter 10 value 51.987861
## iter 20 value 36.592337
## iter 30 value 36.456013
## iter 40 value 36.431679
## iter 50 value 36.403768
## iter 60 value 36.400671
## iter 70 value 36.397634
## iter 80 value 36.396854
## iter 90 value 36.396781
## iter 100 value 36.396700
## final value 36.396700
## stopped after 100 iterations
## # weights: 34
## initial value 329.660847
## iter 10 value 35.848048
## iter 20 value 27.567689
## iter 30 value 22.091387
## iter 40 value 20.115656
## iter 50 value 19.225038
## iter 60 value 18.909831
## iter 70 value 18.507018
## iter 80 value 18.487255
## iter 90 value 18.452722
## iter 100 value 18.338899
## final value 18.338899
## stopped after 100 iterations
## # weights: 56
## initial value 419.117607
## iter 10 value 27.495994
## iter 20 value 20.941925
## iter 30 value 14.476167
## iter 40 value 12.048179
## iter 50 value 10.253878
## iter 60 value 7.624202
## iter 70 value 7.034610
## iter 80 value 7.019065
## iter 90 value 6.991152
## iter 100 value 6.973267
## final value 6.973267
## stopped after 100 iterations
## # weights: 12
## initial value 337.082791
## iter 10 value 41.093191
## iter 20 value 38.747003
## iter 30 value 32.842726
## iter 40 value 29.637886
## iter 50 value 29.621176
## iter 60 value 29.603753
## iter 70 value 29.600528
## iter 80 value 29.598570
## iter 90 value 29.596788
## iter 100 value 29.595872
## final value 29.595872
## stopped after 100 iterations
## # weights: 34
## initial value 342.612257
## iter 10 value 30.782661
## iter 20 value 21.401761
## iter 30 value 20.277952
## iter 40 value 19.481275
## iter 50 value 18.936903
## iter 60 value 18.789701
## iter 70 value 18.659828
## iter 80 value 18.637688
## iter 90 value 18.632417
## iter 100 value 18.631453
## final value 18.631453
## stopped after 100 iterations
## # weights: 56
## initial value 318.832949
## iter 10 value 29.006345
## iter 20 value 26.819401
## iter 30 value 24.601084
## iter 40 value 22.169173
## iter 50 value 22.032442
## iter 60 value 21.875934
## iter 70 value 21.695258
## iter 80 value 21.639906
## iter 90 value 21.487458
## iter 100 value 21.180143
## final value 21.180143
## stopped after 100 iterations
## # weights: 12
## initial value 366.243295
## iter 10 value 68.290228
## iter 20 value 46.105756
## iter 30 value 45.876065
## final value 45.842733
## converged
## # weights: 34
## initial value 429.299172
## iter 10 value 51.387802
## iter 20 value 42.108297
## iter 30 value 38.264316
## iter 40 value 36.710112
## iter 50 value 35.857503
## iter 60 value 35.326832
## iter 70 value 35.277046
## iter 80 value 35.270288
## final value 35.270260
## converged
## # weights: 56
## initial value 348.622565
## iter 10 value 60.267360
## iter 20 value 37.833463
## iter 30 value 36.275569
## iter 40 value 34.859005
## iter 50 value 34.325934
## iter 60 value 34.274962
## iter 70 value 34.208440
## iter 80 value 34.178294
## final value 34.178293
## converged
## # weights: 12
## initial value 338.509067
## iter 10 value 36.411955
## iter 20 value 29.952912
## iter 30 value 29.714104
## iter 40 value 29.701216
## iter 50 value 29.696500
## iter 60 value 29.693649
## iter 70 value 29.693081
## iter 80 value 29.692969
## final value 29.692930
## converged
## # weights: 34
## initial value 467.984092
## iter 10 value 24.310094
## iter 20 value 21.555697
## iter 30 value 20.038811
## iter 40 value 16.784191
## iter 50 value 16.418548
## iter 60 value 16.278828
## iter 70 value 16.213228
## iter 80 value 16.179492
## iter 90 value 16.149731
## iter 100 value 16.075770
## final value 16.075770
## stopped after 100 iterations
## # weights: 56
## initial value 333.033534
## iter 10 value 28.095630
## iter 20 value 19.609004
## iter 30 value 15.649139
## iter 40 value 9.784246
## iter 50 value 8.613313
## iter 60 value 7.874408
## iter 70 value 7.747344
## iter 80 value 7.626360
## iter 90 value 6.793278
## iter 100 value 5.802881
## final value 5.802881
## stopped after 100 iterations
## # weights: 12
## initial value 350.507819
## iter 10 value 42.151537
## iter 20 value 39.843738
## iter 30 value 39.712886
## iter 40 value 39.569480
## iter 50 value 39.475784
## final value 39.475627
## converged
## # weights: 34
## initial value 337.529638
## iter 10 value 45.361427
## iter 20 value 40.098899
## iter 30 value 35.415620
## iter 40 value 32.212177
## iter 50 value 31.233412
## iter 60 value 30.752763
## iter 70 value 27.807388
## iter 80 value 27.251776
## iter 90 value 27.087158
## iter 100 value 27.032602
## final value 27.032602
## stopped after 100 iterations
## # weights: 56
## initial value 366.608305
## iter 10 value 30.266184
## iter 20 value 16.167084
## iter 30 value 12.848775
## iter 40 value 12.648193
## iter 50 value 12.643078
## final value 12.643057
## converged
## # weights: 12
## initial value 312.789982
## iter 10 value 63.443722
## iter 20 value 50.119939
## iter 30 value 49.912730
## final value 49.912724
## converged
## # weights: 34
## initial value 426.806857
## iter 10 value 57.712598
## iter 20 value 45.659152
## iter 30 value 42.848832
## iter 40 value 41.414863
## iter 50 value 39.953397
## iter 60 value 39.602961
## iter 70 value 39.495716
## iter 80 value 39.477708
## final value 39.477707
## converged
## # weights: 56
## initial value 409.658009
## iter 10 value 65.856347
## iter 20 value 42.335663
## iter 30 value 38.847240
## iter 40 value 37.991475
## iter 50 value 37.660651
## iter 60 value 37.519549
## iter 70 value 37.517250
## final value 37.517246
## converged
## # weights: 12
## initial value 360.673122
## iter 10 value 50.486502
## iter 20 value 38.691959
## iter 30 value 37.080744
## iter 40 value 36.371937
## iter 50 value 36.344385
## iter 60 value 36.304465
## iter 70 value 36.299232
## iter 80 value 36.292219
## iter 90 value 36.290972
## iter 100 value 36.290729
## final value 36.290729
## stopped after 100 iterations
## # weights: 34
## initial value 314.249265
## iter 10 value 31.806458
## iter 20 value 18.007996
## iter 30 value 16.008372
## iter 40 value 15.974089
## iter 50 value 15.935297
## iter 60 value 15.007007
## iter 70 value 14.871113
## iter 80 value 14.835302
## iter 90 value 14.818615
## iter 100 value 14.797954
## final value 14.797954
## stopped after 100 iterations
## # weights: 56
## initial value 282.994652
## iter 10 value 32.863933
## iter 20 value 25.242699
## iter 30 value 21.848654
## iter 40 value 20.903148
## iter 50 value 20.089602
## iter 60 value 18.145394
## iter 70 value 17.310628
## iter 80 value 15.757893
## iter 90 value 12.838701
## iter 100 value 10.738107
## final value 10.738107
## stopped after 100 iterations
## # weights: 12
## initial value 310.662028
## iter 10 value 45.067251
## iter 20 value 42.107726
## iter 30 value 40.785565
## iter 40 value 39.403425
## iter 50 value 39.095524
## iter 60 value 39.068557
## iter 70 value 38.842371
## iter 80 value 38.763954
## iter 90 value 38.756277
## iter 100 value 38.699425
## final value 38.699425
## stopped after 100 iterations
## # weights: 34
## initial value 316.398936
## iter 10 value 40.499949
## iter 20 value 31.303517
## iter 30 value 24.999740
## iter 40 value 21.439108
## iter 50 value 20.366338
## iter 60 value 20.062327
## iter 70 value 19.957262
## iter 80 value 19.700825
## iter 90 value 19.623871
## iter 100 value 19.605032
## final value 19.605032
## stopped after 100 iterations
## # weights: 56
## initial value 389.723233
## iter 10 value 45.318806
## iter 20 value 33.639508
## iter 30 value 25.749148
## iter 40 value 20.857779
## iter 50 value 19.809630
## iter 60 value 19.399449
## iter 70 value 19.088599
## iter 80 value 19.025030
## iter 90 value 18.983287
## iter 100 value 18.455398
## final value 18.455398
## stopped after 100 iterations
## # weights: 12
## initial value 327.343382
## iter 10 value 65.937652
## iter 20 value 56.905788
## iter 30 value 52.577441
## iter 40 value 52.533098
## iter 40 value 52.533098
## iter 40 value 52.533098
## final value 52.533098
## converged
## # weights: 34
## initial value 317.243132
## iter 10 value 48.395739
## iter 20 value 44.782081
## iter 30 value 42.471894
## iter 40 value 41.428641
## iter 50 value 41.325394
## final value 41.325034
## converged
## # weights: 56
## initial value 341.538824
## iter 10 value 44.119492
## iter 20 value 40.764450
## iter 30 value 39.780792
## iter 40 value 39.585517
## iter 50 value 38.512155
## iter 60 value 37.672191
## iter 70 value 37.415716
## iter 80 value 37.305387
## iter 90 value 37.304534
## final value 37.304532
## converged
## # weights: 12
## initial value 414.666382
## iter 10 value 55.741794
## iter 20 value 55.142215
## iter 30 value 47.865734
## iter 40 value 44.482343
## iter 50 value 42.141879
## iter 60 value 40.792507
## iter 70 value 40.727730
## iter 80 value 40.708180
## iter 90 value 40.705045
## iter 100 value 40.699397
## final value 40.699397
## stopped after 100 iterations
## # weights: 34
## initial value 454.871632
## iter 10 value 47.867312
## iter 20 value 35.073665
## iter 30 value 29.736968
## iter 40 value 26.233819
## iter 50 value 24.826561
## iter 60 value 24.706896
## iter 70 value 24.651675
## iter 80 value 24.606194
## iter 90 value 24.573879
## iter 100 value 24.562107
## final value 24.562107
## stopped after 100 iterations
## # weights: 56
## initial value 506.277261
## iter 10 value 45.324173
## iter 20 value 31.325272
## iter 30 value 24.083766
## iter 40 value 21.825120
## iter 50 value 20.339440
## iter 60 value 19.310397
## iter 70 value 18.576259
## iter 80 value 18.236524
## iter 90 value 17.997800
## iter 100 value 17.619298
## final value 17.619298
## stopped after 100 iterations
## # weights: 12
## initial value 318.274175
## iter 10 value 44.351135
## iter 20 value 39.860956
## iter 30 value 39.483856
## iter 40 value 39.481330
## iter 50 value 39.479959
## iter 60 value 39.473889
## iter 60 value 39.473889
## final value 39.473889
## converged
## # weights: 34
## initial value 364.502703
## iter 10 value 33.983418
## iter 20 value 31.631476
## iter 30 value 29.560057
## iter 40 value 23.521756
## iter 50 value 22.125949
## iter 60 value 20.852489
## iter 70 value 20.482701
## iter 80 value 20.463065
## iter 90 value 20.453655
## iter 100 value 20.392042
## final value 20.392042
## stopped after 100 iterations
## # weights: 56
## initial value 330.050646
## iter 10 value 34.624621
## iter 20 value 21.144637
## iter 30 value 14.033838
## iter 40 value 11.989197
## iter 50 value 11.422714
## iter 60 value 7.955143
## iter 70 value 7.038181
## iter 80 value 6.695444
## iter 90 value 6.578629
## iter 100 value 6.465123
## final value 6.465123
## stopped after 100 iterations
## # weights: 12
## initial value 342.456337
## iter 10 value 81.247087
## iter 20 value 52.934073
## iter 30 value 47.307460
## iter 40 value 47.130093
## final value 47.130083
## converged
## # weights: 34
## initial value 428.768248
## iter 10 value 56.828014
## iter 20 value 38.680476
## iter 30 value 36.873760
## iter 40 value 36.790497
## iter 50 value 36.783313
## final value 36.783232
## converged
## # weights: 56
## initial value 359.221014
## iter 10 value 42.006330
## iter 20 value 36.994019
## iter 30 value 36.116931
## iter 40 value 35.922179
## iter 50 value 35.187883
## iter 60 value 35.155331
## iter 70 value 34.840015
## iter 80 value 33.640131
## iter 90 value 33.576472
## final value 33.576227
## converged
## # weights: 12
## initial value 373.358462
## iter 10 value 51.078115
## iter 20 value 42.532985
## iter 30 value 39.595553
## iter 40 value 39.564149
## iter 50 value 39.560391
## iter 60 value 39.558739
## iter 70 value 39.557458
## iter 80 value 39.556938
## iter 90 value 39.556513
## iter 100 value 39.555706
## final value 39.555706
## stopped after 100 iterations
## # weights: 34
## initial value 333.215449
## iter 10 value 40.640566
## iter 20 value 40.492335
## iter 30 value 39.220100
## iter 40 value 37.219605
## iter 50 value 37.097285
## iter 60 value 36.819867
## iter 70 value 36.571810
## iter 80 value 36.483523
## iter 90 value 36.183805
## iter 100 value 35.994196
## final value 35.994196
## stopped after 100 iterations
## # weights: 56
## initial value 324.028080
## iter 10 value 32.385498
## iter 20 value 23.511749
## iter 30 value 18.221299
## iter 40 value 16.840219
## iter 50 value 16.635077
## iter 60 value 16.421890
## iter 70 value 16.104300
## iter 80 value 15.918981
## iter 90 value 15.827826
## iter 100 value 15.729256
## final value 15.729256
## stopped after 100 iterations
## # weights: 12
## initial value 372.917410
## iter 10 value 56.345154
## iter 20 value 48.675054
## iter 30 value 45.343551
## iter 40 value 43.739521
## iter 50 value 43.726130
## iter 60 value 43.721540
## iter 70 value 43.719900
## iter 80 value 43.719231
## iter 90 value 43.719122
## iter 100 value 43.719028
## final value 43.719028
## stopped after 100 iterationsresultado_entrenamiento5 <- predict(modelo5,entrenamiento)
resultado_prueba5 <- predict(modelo5,prueba)
# Matriz de Confusión
mcre5 <- confusionMatrix(resultado_entrenamiento5, entrenamiento$Class) # matriz de confusión del resultado del entrenamiento
mcre5## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 347 1
## malignant 9 191
##
## Accuracy : 0.9818
## 95% CI : (0.9667, 0.9912)
## No Information Rate : 0.6496
## P-Value [Acc > NIR] : < 2e-16
##
## Kappa : 0.9603
##
## Mcnemar's Test P-Value : 0.02686
##
## Sensitivity : 0.9747
## Specificity : 0.9948
## Pos Pred Value : 0.9971
## Neg Pred Value : 0.9550
## Prevalence : 0.6496
## Detection Rate : 0.6332
## Detection Prevalence : 0.6350
## Balanced Accuracy : 0.9848
##
## 'Positive' Class : benign
## mcrp5 <- confusionMatrix(resultado_prueba5, prueba$Class) # matriz de confusión del resultado de la prueba
mcrp5## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 85 0
## malignant 3 47
##
## Accuracy : 0.9778
## 95% CI : (0.9364, 0.9954)
## No Information Rate : 0.6519
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.9518
##
## Mcnemar's Test P-Value : 0.2482
##
## Sensitivity : 0.9659
## Specificity : 1.0000
## Pos Pred Value : 1.0000
## Neg Pred Value : 0.9400
## Prevalence : 0.6519
## Detection Rate : 0.6296
## Detection Prevalence : 0.6296
## Balanced Accuracy : 0.9830
##
## 'Positive' Class : benign
## modelo6 <- train(Class ~ ., data=entrenamiento,
method = "rf",
preProcess=c("scale","center"),
trControl = trainControl(method="cv", number=10),
tuneGrid = expand.grid(mtry = c(2,4,6)) #Cambiar
)
resultado_entrenamiento6 <- predict(modelo6,entrenamiento)
resultado_prueba6 <- predict(modelo6,prueba)
# Matriz de Confusión
mcre6 <- confusionMatrix(resultado_entrenamiento6, entrenamiento$Class) # matriz de confusión del resultado del entrenamiento
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
## mcrp6 <- confusionMatrix(resultado_prueba6, prueba$Class) # matriz de confusión del resultado de la prueba
mcrp6## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 85 1
## malignant 3 46
##
## Accuracy : 0.9704
## 95% CI : (0.9259, 0.9919)
## No Information Rate : 0.6519
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.9354
##
## Mcnemar's Test P-Value : 0.6171
##
## Sensitivity : 0.9659
## Specificity : 0.9787
## Pos Pred Value : 0.9884
## Neg Pred Value : 0.9388
## Prevalence : 0.6519
## Detection Rate : 0.6296
## Detection Prevalence : 0.6370
## Balanced Accuracy : 0.9723
##
## 'Positive' Class : benign
## resultados <- data.frame(
"svmLinear" = c(mcre1$overall["Acurracy"], mcrp1$overall["Acurracy"]),
"svmRadial" = c(mcre2$overall["Acurracy"], mcrp2$overall["Acurracy"]),
"svmpoly" = c(mcre3$overall["Acurracy"], mcrp3$overall["Acurracy"]),
"rpart" = c(mcre4$overall["Acurracy"], mcrp4$overall["Acurracy"]),
"nnet" = c(mcre5$overall["Acurracy"], mcrp5$overall["Acurracy"]),
"rf" = c(mcre6$overall["Acurracy"], mcrp6$overall["Acurracy"])
)
rownames(resultados) <- c("Precision de entrenamiento", "Precision de prueba")
resultados## svmLinear svmRadial svmpoly rpart nnet rf
## Precision de entrenamiento NA NA NA NA NA NA
## Precision de prueba NA NA NA NA NA NA##Basándonos únicamente en la precisión, el modelo de Random Forest parece tener el mejor rendimiento en el conjunto de entrenamiento, ya que alcanza una precisión del 100%. Sin embargo, debemos tener en cuenta que esto podría indicar un sobreajuste al conjunto de entrenamiento.
##En cuanto a los modelos SVM, aunque el SVM Radial tiene una precisión ligeramente más baja en el conjunto de prueba en comparación con el SVM Linear y el SVM Polinomial, parece generalizar mejor, ya que su precisión de entrenamiento y prueba son comparativamente altas y cercanas.