ML: BreastCancer

Teoría

El paquete caret (Clasification And Regression Training) es un paquete integral con una amplia variedad de algoritmos para el aprendizaje automático.

Instalar paquetes y llamar librerías

#file.choose()
#install.packages("caret") #Algoritmos de aprendizaje
library(caret)
#install.packages("datasets") #Usar la base de datos "Iris"
library(datasets)
#install.packages("ggplot2") #Gráficas con mejor diseño
library(ggplot2)
#install.packages("lattice") #Crear gráficos
library(lattice)
#install.packages("DataExplorer") #Crear gráficos
library(DataExplorer)
library(mlbench)
library(tidyverse)

Crear base de datos

data("BreastCancer")
df <- data.frame(BreastCancer)
df <- df %>% select(-Id)
#View(df)

#Análisis exploratorio

summary(df)

##   Cl.thickness   Cell.size     Cell.shape  Marg.adhesion  Epith.c.size
##  1      :145   1      :384   1      :353   1      :407   2      :386  
##  5      :130   10     : 67   2      : 59   2      : 58   3      : 72  
##  3      :108   3      : 52   10     : 58   3      : 58   4      : 48  
##  4      : 80   2      : 45   3      : 56   10     : 55   1      : 47  
##  10     : 69   4      : 40   4      : 44   4      : 33   6      : 41  
##  2      : 50   5      : 30   5      : 34   8      : 25   5      : 39  
##  (Other):117   (Other): 81   (Other): 95   (Other): 63   (Other): 66  
##   Bare.nuclei   Bl.cromatin  Normal.nucleoli    Mitoses          Class    
##  1      :402   2      :166   1      :443     1      :579   benign   :458  
##  10     :132   3      :165   10     : 61     2      : 35   malignant:241  
##  2      : 30   1      :152   3      : 44     3      : 33                  
##  5      : 30   7      : 73   2      : 36     10     : 14                  
##  3      : 28   4      : 40   8      : 24     4      : 12                  
##  (Other): 61   5      : 34   6      : 22     7      :  9                  
##  NA's   : 16   (Other): 69   (Other): 69     (Other): 17

str(df)

## 'data.frame':    699 obs. of  10 variables:
##  $ 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 ...

boxplot(df)

plot_missing(df)

#plot_histogram(df)
plot_correlation(df)

#create_report(df)
df <- na.omit(df)

Nota: La variable que queremos predecir debe tener formato de FACTOR

Partir datos 80/20

set.seed(123)
renglones_entrenamiento <- createDataPartition(df$Class, p=.8, list=FALSE)

entrenamiento <- df[-renglones_entrenamiento, ]
prueba <- df[renglones_entrenamiento, ]

  
entrenamiento$Class <- as.factor(entrenamiento$Class)
prueba$Class <- as.factor(prueba$Class)

Distintos tipos de métodos para modelar

Los métodos más utilizados para modelar aprendizaje automático son: * SVM. Support Vector Machine o Máquina de Vectores de Soporte. Hay varios subtipos: Lineal (svmLineal), Rdial (svmRadial), Polinómico (svmPoly), etc. * Árbol de Decisión. rpart * Redes Neuronales. nnet * Random Forest o Bosques Aleatorios. rf * Random Forest o Bosques Aleatorios. rf

1. Modelo con el método svmLineal

#install.packages("kernlab")
library(caret)

modelo1 <- train(Class ~ ., data=entrenamiento, 
                method = "svmLinear", 
                preProcess = c("scale", "center"),
                trControl = trainControl(method="cv", number=10),
                tuneGrid = data.frame(C=1) #Cuando es svmLinear
                )

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses6,
## Mitoses7

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bare.nuclei9, Bl.cromatin9,
## Mitoses4, Mitoses7

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses5,
## Mitoses7

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7,
## Mitoses8

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

resultado_entrenamiento1 <- predict(modelo1, entrenamiento)

resultado_prueba1 <- predict(modelo1, prueba)

#Matriz de Consufión 
mcre1 <- confusionMatrix(resultado_entrenamiento1,entrenamiento$Class) #Matriz de confusion de resultado de entrenamiento
mcre1

## Confusion Matrix and Statistics
## 
##            Reference
## Prediction  benign malignant
##   benign        88         0
##   malignant      0        47
##                                     
##                Accuracy : 1         
##                  95% CI : (0.973, 1)
##     No Information Rate : 0.6519    
##     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 : 0.6519    
##          Detection Rate : 0.6519    
##    Detection Prevalence : 0.6519    
##       Balanced Accuracy : 1.0000    
##                                     
##        'Positive' Class : benign    
## 

mcrp1 <- confusionMatrix(resultado_prueba1, prueba$Class)  #Matriz de confusion de resultado de prueba
mcrp1

## Confusion Matrix and Statistics
## 
##            Reference
## Prediction  benign malignant
##   benign       346        13
##   malignant     10       179
##                                           
##                Accuracy : 0.958           
##                  95% CI : (0.9377, 0.9732)
##     No Information Rate : 0.6496          
##     P-Value [Acc > NIR] : <2e-16          
##                                           
##                   Kappa : 0.9075          
##                                           
##  Mcnemar's Test P-Value : 0.6767          
##                                           
##             Sensitivity : 0.9719          
##             Specificity : 0.9323          
##          Pos Pred Value : 0.9638          
##          Neg Pred Value : 0.9471          
##              Prevalence : 0.6496          
##          Detection Rate : 0.6314          
##    Detection Prevalence : 0.6551          
##       Balanced Accuracy : 0.9521          
##                                           
##        'Positive' Class : benign          
## 

2. Modelo con el método svmRadial

modelo2 <- train(Class ~ ., data=entrenamiento, 
                method = "svmRadial", 
                preProcess = c("scale", "center"),
                trControl = trainControl(method="cv", number=10),
                tuneGrid = data.frame(sigma=1,C=1) #Cambiar
                )

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bare.nuclei9, Bl.cromatin9,
## Mitoses4, Mitoses7

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses5,
## Mitoses7

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses6,
## Mitoses7

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7,
## Mitoses8

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

resultado_entrenamiento2 <- predict(modelo2, entrenamiento)
resultado_prueba2 <- predict(modelo2, prueba)

#Matriz de Consufión 
mcre2 <- confusionMatrix(resultado_entrenamiento2,entrenamiento$Class) #Matriz de confusion de resultado de entrenamiento
mcre2

## Confusion Matrix and Statistics
## 
##            Reference
## Prediction  benign malignant
##   benign        88         0
##   malignant      0        47
##                                     
##                Accuracy : 1         
##                  95% CI : (0.973, 1)
##     No Information Rate : 0.6519    
##     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 : 0.6519    
##          Detection Rate : 0.6519    
##    Detection Prevalence : 0.6519    
##       Balanced Accuracy : 1.0000    
##                                     
##        'Positive' Class : benign    
## 

mcrp2 <- confusionMatrix(resultado_prueba2, prueba$Class)  #Matriz de confusion de resultado de prueba
mcrp2

## Confusion Matrix and Statistics
## 
##            Reference
## Prediction  benign malignant
##   benign       356       192
##   malignant      0         0
##                                           
##                Accuracy : 0.6496          
##                  95% CI : (0.6081, 0.6896)
##     No Information Rate : 0.6496          
##     P-Value [Acc > NIR] : 0.5196          
##                                           
##                   Kappa : 0               
##                                           
##  Mcnemar's Test P-Value : <2e-16          
##                                           
##             Sensitivity : 1.0000          
##             Specificity : 0.0000          
##          Pos Pred Value : 0.6496          
##          Neg Pred Value :    NaN          
##              Prevalence : 0.6496          
##          Detection Rate : 0.6496          
##    Detection Prevalence : 1.0000          
##       Balanced Accuracy : 0.5000          
##                                           
##        'Positive' Class : benign          
## 

3. Modelo con el método svmPoly

modelo3 <- 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))  # Adjust values as needed

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bare.nuclei9, Bl.cromatin9,
## Mitoses4, Mitoses5, Mitoses7

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses6,
## Mitoses7

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7,
## Mitoses8

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.

resultado_entrenamiento3 <- predict(modelo3, entrenamiento)
resultado_prueba3 <- predict(modelo3, prueba)

#Matriz de Consufión 
mcre3 <- confusionMatrix(resultado_entrenamiento3,entrenamiento$Class) #Matriz de confusion de resultado de entrenamiento
mcre3

## Confusion Matrix and Statistics
## 
##            Reference
## Prediction  benign malignant
##   benign        88         0
##   malignant      0        47
##                                     
##                Accuracy : 1         
##                  95% CI : (0.973, 1)
##     No Information Rate : 0.6519    
##     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 : 0.6519    
##          Detection Rate : 0.6519    
##    Detection Prevalence : 0.6519    
##       Balanced Accuracy : 1.0000    
##                                     
##        'Positive' Class : benign    
## 

mcrp3 <- confusionMatrix(resultado_prueba3, prueba$Class)  #Matriz de confusion de resultado de prueba
mcrp3

## Confusion Matrix and Statistics
## 
##            Reference
## Prediction  benign malignant
##   benign       346        13
##   malignant     10       179
##                                           
##                Accuracy : 0.958           
##                  95% CI : (0.9377, 0.9732)
##     No Information Rate : 0.6496          
##     P-Value [Acc > NIR] : <2e-16          
##                                           
##                   Kappa : 0.9075          
##                                           
##  Mcnemar's Test P-Value : 0.6767          
##                                           
##             Sensitivity : 0.9719          
##             Specificity : 0.9323          
##          Pos Pred Value : 0.9638          
##          Neg Pred Value : 0.9471          
##              Prevalence : 0.6496          
##          Detection Rate : 0.6314          
##    Detection Prevalence : 0.6551          
##       Balanced Accuracy : 0.9521          
##                                           
##        'Positive' Class : benign          
## 

4. Modelo con el método rpart

modelo4 <- train(Class ~ ., data=entrenamiento, 
                method = "rpart", 
                preProcess = c("scale", "center"),
                trControl = trainControl(method="cv", number=10),
                tuneLength = 10 #Cambiar
                )

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7,
## Mitoses8

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bare.nuclei9, Bl.cromatin9,
## Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses5,
## Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses6,
## Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

resultado_entrenamiento4 <- predict(modelo4, entrenamiento)
resultado_prueba4 <- predict(modelo4, prueba)

#Matriz de Consufión 
mcre4 <- confusionMatrix(resultado_entrenamiento4,entrenamiento$Class) #Matriz de confusion de resultado de entrenamiento
mcre4

## Confusion Matrix and Statistics
## 
##            Reference
## Prediction  benign malignant
##   benign        86         6
##   malignant      2        41
##                                           
##                Accuracy : 0.9407          
##                  95% CI : (0.8866, 0.9741)
##     No Information Rate : 0.6519          
##     P-Value [Acc > NIR] : 1.347e-15       
##                                           
##                   Kappa : 0.8668          
##                                           
##  Mcnemar's Test P-Value : 0.2888          
##                                           
##             Sensitivity : 0.9773          
##             Specificity : 0.8723          
##          Pos Pred Value : 0.9348          
##          Neg Pred Value : 0.9535          
##              Prevalence : 0.6519          
##          Detection Rate : 0.6370          
##    Detection Prevalence : 0.6815          
##       Balanced Accuracy : 0.9248          
##                                           
##        'Positive' Class : benign          
## 

mcrp4 <- confusionMatrix(resultado_prueba4, prueba$Class)  #Matriz de confusion de resultado de prueba
mcrp4

## Confusion Matrix and Statistics
## 
##            Reference
## Prediction  benign malignant
##   benign       347        31
##   malignant      9       161
##                                           
##                Accuracy : 0.927           
##                  95% CI : (0.9019, 0.9473)
##     No Information Rate : 0.6496          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.8353          
##                                           
##  Mcnemar's Test P-Value : 0.0008989       
##                                           
##             Sensitivity : 0.9747          
##             Specificity : 0.8385          
##          Pos Pred Value : 0.9180          
##          Neg Pred Value : 0.9471          
##              Prevalence : 0.6496          
##          Detection Rate : 0.6332          
##    Detection Prevalence : 0.6898          
##       Balanced Accuracy : 0.9066          
##                                           
##        'Positive' Class : benign          
## 

5. Modelo con el metodo nnet

modelo5 <- train(Class ~ ., data=entrenamiento, 
                method = "nnet", 
                preProcess = c("scale", "center"),
                trControl = trainControl(method="cv", number=10)
                )

## Warning in preProcess.default(method = c("scale", "center"), x =
## structure(c(-0.055048188256318, : These variables have zero variances:
## Bl.cromatin9, Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bare.nuclei9, Bl.cromatin9,
## Mitoses4, Mitoses7

## # weights:  83
## initial  value 74.556563 
## iter  10 value 13.604556
## iter  20 value 13.592591
## iter  30 value 11.022652
## iter  40 value 11.021928
## final  value 11.021910 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bare.nuclei9, Bl.cromatin9,
## Mitoses4, Mitoses7

## # weights:  247
## initial  value 84.092166 
## iter  10 value 14.175167
## iter  20 value 8.148974
## iter  30 value 4.763820
## iter  40 value 4.749747
## iter  50 value 4.749491
## final  value 4.749481 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bare.nuclei9, Bl.cromatin9,
## Mitoses4, Mitoses7

## # weights:  411
## initial  value 93.939341 
## iter  10 value 13.802829
## iter  20 value 2.004212
## iter  30 value 1.509982
## iter  40 value 1.407032
## iter  50 value 0.267967
## iter  60 value 0.035845
## iter  70 value 0.021422
## iter  80 value 0.004086
## iter  90 value 0.001719
## iter 100 value 0.001605
## final  value 0.001605 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bare.nuclei9, Bl.cromatin9,
## Mitoses4, Mitoses7

## # weights:  83
## initial  value 103.578534 
## iter  10 value 21.671572
## iter  20 value 10.636351
## iter  30 value 10.112557
## iter  40 value 10.107159
## final  value 10.107134 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bare.nuclei9, Bl.cromatin9,
## Mitoses4, Mitoses7

## # weights:  247
## initial  value 84.788372 
## iter  10 value 25.357033
## iter  20 value 6.533431
## iter  30 value 5.415313
## iter  40 value 5.353170
## iter  50 value 5.342231
## iter  60 value 5.339957
## iter  70 value 5.339414
## iter  80 value 5.339235
## iter  90 value 5.339170
## final  value 5.339161 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bare.nuclei9, Bl.cromatin9,
## Mitoses4, Mitoses7

## # weights:  411
## initial  value 86.286927 
## iter  10 value 6.655475
## iter  20 value 4.407253
## iter  30 value 4.326996
## iter  40 value 4.325436
## final  value 4.325436 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bare.nuclei9, Bl.cromatin9,
## Mitoses4, Mitoses7

## # weights:  83
## initial  value 99.424309 
## iter  10 value 18.131230
## iter  20 value 15.972076
## iter  30 value 15.961252
## iter  40 value 11.064423
## iter  50 value 8.170670
## iter  60 value 8.167909
## iter  70 value 0.430572
## iter  80 value 0.069528
## iter  90 value 0.061169
## iter 100 value 0.056820
## final  value 0.056820 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bare.nuclei9, Bl.cromatin9,
## Mitoses4, Mitoses7

## # weights:  247
## initial  value 108.324739 
## iter  10 value 24.688868
## iter  20 value 8.462536
## iter  30 value 4.461838
## iter  40 value 2.753490
## iter  50 value 2.076137
## iter  60 value 2.011787
## iter  70 value 0.142743
## iter  80 value 0.091036
## iter  90 value 0.075988
## iter 100 value 0.067904
## final  value 0.067904 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bare.nuclei9, Bl.cromatin9,
## Mitoses4, Mitoses7

## # weights:  411
## initial  value 121.036369 
## iter  10 value 0.186363
## iter  20 value 0.106247
## iter  30 value 0.074670
## iter  40 value 0.067601
## iter  50 value 0.060116
## iter  60 value 0.052483
## iter  70 value 0.048390
## iter  80 value 0.042532
## iter  90 value 0.038946
## iter 100 value 0.031947
## final  value 0.031947 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  83
## initial  value 88.915252 
## iter  10 value 16.176759
## iter  20 value 0.022469
## final  value 0.000090 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  247
## initial  value 70.594708 
## final  value 8.135926 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  411
## initial  value 78.302526 
## iter  10 value 0.458691
## iter  20 value 0.043318
## iter  30 value 0.002411
## iter  40 value 0.000214
## final  value 0.000083 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  83
## initial  value 82.781959 
## iter  10 value 22.542995
## iter  20 value 14.905617
## iter  30 value 10.106471
## iter  40 value 10.102207
## final  value 10.102206 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  247
## initial  value 99.164787 
## iter  10 value 9.828203
## iter  20 value 5.347100
## iter  30 value 5.315059
## iter  40 value 5.313924
## iter  50 value 5.313905
## final  value 5.313904 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  411
## initial  value 95.061549 
## iter  10 value 9.622697
## iter  20 value 4.327455
## iter  30 value 4.295029
## iter  40 value 4.292344
## iter  50 value 4.292265
## final  value 4.292265 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  83
## initial  value 87.960384 
## iter  10 value 0.079519
## iter  20 value 0.074786
## iter  30 value 0.068294
## iter  40 value 0.065043
## iter  50 value 0.062735
## iter  60 value 0.061642
## iter  70 value 0.058560
## iter  80 value 0.057696
## iter  90 value 0.056992
## iter 100 value 0.056678
## final  value 0.056678 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  247
## initial  value 83.325052 
## iter  10 value 0.428821
## iter  20 value 0.091309
## iter  30 value 0.086417
## iter  40 value 0.078290
## iter  50 value 0.074184
## iter  60 value 0.069716
## iter  70 value 0.066243
## iter  80 value 0.061268
## iter  90 value 0.059141
## iter 100 value 0.055344
## final  value 0.055344 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  411
## initial  value 84.428297 
## iter  10 value 14.602655
## iter  20 value 4.886415
## iter  30 value 4.821482
## iter  40 value 4.787909
## iter  50 value 0.511674
## iter  60 value 0.095895
## iter  70 value 0.076887
## iter  80 value 0.047410
## iter  90 value 0.043408
## iter 100 value 0.040317
## final  value 0.040317 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses6,
## Mitoses7

## # weights:  83
## initial  value 75.023120 
## iter  10 value 8.126545
## iter  20 value 0.032748
## iter  30 value 0.000338
## final  value 0.000095 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses6,
## Mitoses7

## # weights:  247
## initial  value 85.198386 
## iter  10 value 15.448146
## iter  20 value 0.020983
## iter  30 value 0.003228
## iter  40 value 0.001236
## final  value 0.000097 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses6,
## Mitoses7

## # weights:  411
## initial  value 96.521258 
## iter  10 value 6.746261
## iter  20 value 0.068883
## iter  30 value 0.000676
## final  value 0.000041 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses6,
## Mitoses7

## # weights:  83
## initial  value 89.364695 
## iter  10 value 26.935017
## iter  20 value 12.058451
## iter  30 value 10.134778
## iter  40 value 10.092725
## final  value 10.092657 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses6,
## Mitoses7

## # weights:  247
## initial  value 117.660848 
## iter  10 value 12.016355
## iter  20 value 5.911153
## iter  30 value 5.492425
## iter  40 value 5.483564
## final  value 5.483551 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses6,
## Mitoses7

## # weights:  411
## initial  value 93.853209 
## iter  10 value 6.346432
## iter  20 value 4.424135
## iter  30 value 4.406026
## iter  40 value 4.405557
## final  value 4.405546 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses6,
## Mitoses7

## # weights:  83
## initial  value 82.576498 
## iter  10 value 7.289674
## iter  20 value 4.794485
## iter  30 value 4.790121
## iter  40 value 4.787034
## iter  50 value 4.785704
## iter  60 value 4.785119
## iter  70 value 4.784857
## iter  80 value 4.783923
## iter  90 value 0.082632
## iter 100 value 0.056851
## final  value 0.056851 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses6,
## Mitoses7

## # weights:  247
## initial  value 71.677644 
## iter  10 value 2.488526
## iter  20 value 2.105332
## iter  30 value 1.573908
## iter  40 value 1.539433
## iter  50 value 0.241704
## iter  60 value 0.089813
## iter  70 value 0.086188
## iter  80 value 0.071074
## iter  90 value 0.059270
## iter 100 value 0.051431
## final  value 0.051431 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses6,
## Mitoses7

## # weights:  411
## initial  value 149.605022 
## iter  10 value 0.444839
## iter  20 value 0.195477
## iter  30 value 0.067929
## iter  40 value 0.052476
## iter  50 value 0.044094
## iter  60 value 0.037553
## iter  70 value 0.032090
## iter  80 value 0.027921
## iter  90 value 0.026342
## iter 100 value 0.024544
## final  value 0.024544 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7,
## Mitoses8

## # weights:  83
## initial  value 99.634357 
## iter  10 value 0.358449
## iter  20 value 0.040475
## iter  30 value 0.001423
## final  value 0.000091 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7,
## Mitoses8

## # weights:  247
## initial  value 88.547522 
## iter  10 value 0.179348
## iter  20 value 0.008128
## final  value 0.000098 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7,
## Mitoses8

## # weights:  411
## initial  value 88.339385 
## iter  10 value 1.248014
## iter  20 value 0.004323
## iter  30 value 0.000821
## iter  40 value 0.000360
## iter  50 value 0.000113
## iter  50 value 0.000056
## iter  50 value 0.000056
## final  value 0.000056 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7,
## Mitoses8

## # weights:  83
## initial  value 80.833813 
## iter  10 value 13.107085
## iter  20 value 10.219554
## iter  30 value 10.100906
## iter  40 value 10.100740
## iter  40 value 10.100740
## iter  40 value 10.100740
## final  value 10.100740 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7,
## Mitoses8

## # weights:  247
## initial  value 83.871019 
## iter  10 value 35.420639
## iter  20 value 16.291470
## iter  30 value 10.341697
## iter  40 value 7.822438
## iter  50 value 7.529469
## iter  60 value 7.515349
## iter  70 value 7.513623
## iter  80 value 7.512741
## iter  90 value 7.510252
## iter 100 value 7.505129
## final  value 7.505129 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7,
## Mitoses8

## # weights:  411
## initial  value 95.265070 
## iter  10 value 6.842444
## iter  20 value 4.347544
## iter  30 value 4.320377
## iter  40 value 4.318956
## final  value 4.318952 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7,
## Mitoses8

## # weights:  83
## initial  value 84.427040 
## iter  10 value 22.230673
## iter  20 value 22.063129
## iter  30 value 22.047015
## iter  40 value 22.038565
## iter  50 value 20.155350
## iter  60 value 19.944050
## iter  70 value 18.132255
## iter  80 value 15.972637
## iter  90 value 15.963156
## iter 100 value 8.334774
## final  value 8.334774 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7,
## Mitoses8

## # weights:  247
## initial  value 79.466354 
## iter  10 value 11.001224
## iter  20 value 8.495085
## iter  30 value 8.210333
## iter  40 value 8.160702
## iter  50 value 2.067495
## iter  60 value 0.150466
## iter  70 value 0.111353
## iter  80 value 0.101588
## iter  90 value 0.082953
## iter 100 value 0.076867
## final  value 0.076867 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7,
## Mitoses8

## # weights:  411
## initial  value 80.871010 
## iter  10 value 3.295260
## iter  20 value 2.703390
## iter  30 value 2.028572
## iter  40 value 0.346626
## iter  50 value 0.126171
## iter  60 value 0.117624
## iter  70 value 0.087654
## iter  80 value 0.081323
## iter  90 value 0.063109
## iter 100 value 0.054239
## final  value 0.054239 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  83
## initial  value 99.025211 
## iter  10 value 4.809669
## iter  20 value 4.777829
## iter  30 value 4.773244
## iter  40 value 4.772826
## iter  50 value 4.772772
## iter  60 value 4.772741
## final  value 4.772739 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  247
## initial  value 79.025818 
## iter  10 value 17.934202
## iter  20 value 17.753676
## iter  30 value 13.317376
## iter  40 value 13.212743
## iter  50 value 12.743741
## iter  60 value 4.792352
## iter  70 value 4.765476
## iter  80 value 3.819086
## iter  90 value 3.819047
## iter 100 value 3.819045
## final  value 3.819045 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  411
## initial  value 87.374444 
## iter  10 value 0.151308
## iter  20 value 0.008651
## iter  30 value 0.000166
## iter  30 value 0.000092
## iter  30 value 0.000092
## final  value 0.000092 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  83
## initial  value 86.773904 
## iter  10 value 14.159342
## iter  20 value 11.026509
## iter  30 value 10.616589
## iter  40 value 10.613356
## final  value 10.613329 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  247
## initial  value 89.253691 
## iter  10 value 15.595306
## iter  20 value 5.926808
## iter  30 value 5.542267
## iter  40 value 5.503107
## iter  50 value 5.499264
## iter  60 value 5.499195
## final  value 5.499194 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  411
## initial  value 93.741434 
## iter  10 value 7.117839
## iter  20 value 4.811985
## iter  30 value 4.643247
## iter  40 value 4.633003
## iter  50 value 4.632838
## final  value 4.632831 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  83
## initial  value 84.398454 
## iter  10 value 7.153191
## iter  20 value 3.924413
## iter  30 value 0.133254
## iter  40 value 0.062928
## iter  50 value 0.058864
## iter  60 value 0.056879
## iter  70 value 0.056635
## iter  80 value 0.056191
## iter  90 value 0.056051
## iter 100 value 0.055946
## final  value 0.055946 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  247
## initial  value 83.790622 
## iter  10 value 7.447761
## iter  20 value 4.212299
## iter  30 value 1.538613
## iter  40 value 1.502912
## iter  50 value 1.488952
## iter  60 value 0.765699
## iter  70 value 0.111734
## iter  80 value 0.094161
## iter  90 value 0.085748
## iter 100 value 0.072514
## final  value 0.072514 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  411
## initial  value 75.271323 
## iter  10 value 0.124742
## iter  20 value 0.096831
## iter  30 value 0.062974
## iter  40 value 0.050697
## iter  50 value 0.044981
## iter  60 value 0.035169
## iter  70 value 0.032269
## iter  80 value 0.029640
## iter  90 value 0.026676
## iter 100 value 0.024170
## final  value 0.024170 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  83
## initial  value 89.420591 
## iter  10 value 5.510601
## iter  20 value 4.777149
## iter  30 value 4.752890
## iter  40 value 4.750752
## iter  50 value 4.749858
## iter  60 value 4.749662
## iter  70 value 4.749571
## iter  80 value 4.749496
## final  value 4.749494 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  247
## initial  value 91.089194 
## iter  10 value 10.590777
## iter  20 value 5.264904
## iter  30 value 0.559093
## iter  40 value 0.011248
## iter  50 value 0.000679
## final  value 0.000091 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  411
## initial  value 77.486363 
## iter  10 value 3.007672
## iter  20 value 0.043207
## iter  30 value 0.000549
## final  value 0.000098 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  83
## initial  value 101.002515 
## iter  10 value 15.108441
## iter  20 value 10.829522
## iter  30 value 10.655853
## iter  40 value 10.645451
## iter  50 value 10.644770
## iter  60 value 10.644751
## final  value 10.644751 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  247
## initial  value 123.746719 
## iter  10 value 22.826081
## iter  20 value 6.822582
## iter  30 value 5.464881
## iter  40 value 5.411394
## iter  50 value 5.411155
## final  value 5.411138 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  411
## initial  value 87.535714 
## iter  10 value 9.059006
## iter  20 value 4.800586
## iter  30 value 4.525886
## iter  40 value 4.509962
## iter  50 value 4.509936
## final  value 4.509934 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  83
## initial  value 83.266244 
## iter  10 value 4.915534
## iter  20 value 0.087969
## iter  30 value 0.059147
## iter  40 value 0.057721
## iter  50 value 0.056343
## iter  60 value 0.055569
## iter  70 value 0.055316
## iter  80 value 0.055109
## iter  90 value 0.054996
## iter 100 value 0.054896
## final  value 0.054896 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  247
## initial  value 90.050942 
## iter  10 value 3.967596
## iter  20 value 0.133511
## iter  30 value 0.128595
## iter  40 value 0.096133
## iter  50 value 0.074446
## iter  60 value 0.064164
## iter  70 value 0.055065
## iter  80 value 0.048282
## iter  90 value 0.041855
## iter 100 value 0.036347
## final  value 0.036347 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  411
## initial  value 95.881209 
## iter  10 value 5.214778
## iter  20 value 1.597400
## iter  30 value 0.378258
## iter  40 value 0.185579
## iter  50 value 0.167508
## iter  60 value 0.139514
## iter  70 value 0.117551
## iter  80 value 0.101544
## iter  90 value 0.094384
## iter 100 value 0.082829
## final  value 0.082829 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  83
## initial  value 78.645540 
## iter  10 value 18.944560
## iter  20 value 18.217741
## iter  30 value 18.217033
## iter  30 value 18.217033
## iter  30 value 18.217033
## final  value 18.217033 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  247
## initial  value 88.779024 
## iter  10 value 4.319826
## iter  20 value 0.003459
## iter  30 value 0.000717
## final  value 0.000051 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  411
## initial  value 77.640725 
## iter  10 value 9.891617
## iter  20 value 4.693309
## iter  30 value 1.211581
## iter  40 value 0.005191
## iter  50 value 0.000611
## iter  60 value 0.000288
## iter  70 value 0.000259
## iter  80 value 0.000230
## final  value 0.000050 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  83
## initial  value 81.078437 
## iter  10 value 15.484028
## iter  20 value 10.198100
## iter  30 value 10.152352
## iter  40 value 10.151644
## final  value 10.151641 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  247
## initial  value 92.635873 
## iter  10 value 26.979595
## iter  20 value 7.650078
## iter  30 value 5.615967
## iter  40 value 5.375609
## iter  50 value 5.351347
## iter  60 value 5.350352
## iter  70 value 5.350244
## iter  80 value 5.350238
## final  value 5.350238 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  411
## initial  value 100.998227 
## iter  10 value 12.192258
## iter  20 value 5.388434
## iter  30 value 4.462917
## iter  40 value 4.430710
## iter  50 value 4.430087
## final  value 4.430085 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  83
## initial  value 93.127457 
## iter  10 value 16.033000
## iter  20 value 11.129489
## iter  30 value 8.261892
## iter  40 value 8.217702
## iter  50 value 8.215042
## iter  60 value 0.171239
## iter  70 value 0.063698
## iter  80 value 0.060130
## iter  90 value 0.057077
## iter 100 value 0.056203
## final  value 0.056203 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  247
## initial  value 77.411629 
## iter  10 value 9.411410
## iter  20 value 8.457549
## iter  30 value 8.304275
## iter  40 value 8.238031
## iter  50 value 8.223375
## iter  60 value 2.195595
## iter  70 value 1.477831
## iter  80 value 0.107007
## iter  90 value 0.069876
## iter 100 value 0.060784
## final  value 0.060784 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  411
## initial  value 118.065670 
## iter  10 value 3.372659
## iter  20 value 0.198632
## iter  30 value 0.166894
## iter  40 value 0.147057
## iter  50 value 0.122810
## iter  60 value 0.108507
## iter  70 value 0.067207
## iter  80 value 0.051992
## iter  90 value 0.044622
## iter 100 value 0.038493
## final  value 0.038493 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  83
## initial  value 88.505724 
## iter  10 value 19.470815
## iter  20 value 17.404777
## iter  30 value 17.395375
## iter  40 value 17.393461
## iter  50 value 17.393328
## iter  60 value 14.874626
## iter  70 value 13.452787
## iter  80 value 13.451967
## iter  90 value 13.450047
## iter 100 value 8.169665
## final  value 8.169665 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  247
## initial  value 92.100501 
## iter  10 value 2.131148
## iter  20 value 0.186156
## iter  30 value 0.006364
## final  value 0.000051 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  411
## initial  value 75.028950 
## iter  10 value 0.211835
## iter  20 value 0.047533
## iter  30 value 0.000726
## final  value 0.000089 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  83
## initial  value 81.497192 
## iter  10 value 29.729416
## iter  20 value 13.050973
## iter  30 value 10.186826
## iter  40 value 10.177992
## final  value 10.177991 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  247
## initial  value 100.078171 
## iter  10 value 16.833723
## iter  20 value 6.199342
## iter  30 value 5.692799
## iter  40 value 5.593955
## iter  50 value 5.591083
## iter  60 value 5.590968
## iter  70 value 5.590924
## final  value 5.590923 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  411
## initial  value 91.217533 
## iter  10 value 28.368524
## iter  20 value 5.112483
## iter  30 value 4.509600
## iter  40 value 4.419733
## iter  50 value 4.414387
## iter  60 value 4.413967
## iter  70 value 4.413964
## final  value 4.413963 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  83
## initial  value 78.712964 
## iter  10 value 19.300074
## iter  20 value 12.642973
## iter  30 value 8.201686
## iter  40 value 8.183742
## iter  50 value 8.178196
## iter  60 value 8.173883
## iter  70 value 8.173058
## iter  80 value 8.171417
## iter  90 value 8.170625
## iter 100 value 8.170058
## final  value 8.170058 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  247
## initial  value 75.177306 
## iter  10 value 2.243684
## iter  20 value 1.534463
## iter  30 value 0.433129
## iter  40 value 0.154217
## iter  50 value 0.144209
## iter  60 value 0.085468
## iter  70 value 0.061275
## iter  80 value 0.053074
## iter  90 value 0.041694
## iter 100 value 0.036679
## final  value 0.036679 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  411
## initial  value 81.260625 
## iter  10 value 3.389063
## iter  20 value 1.527312
## iter  30 value 0.174777
## iter  40 value 0.135477
## iter  50 value 0.070857
## iter  60 value 0.054661
## iter  70 value 0.047655
## iter  80 value 0.039987
## iter  90 value 0.035506
## iter 100 value 0.032602
## final  value 0.032602 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses5,
## Mitoses7

## # weights:  83
## initial  value 84.575494 
## iter  10 value 18.085247
## final  value 18.085198 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses5,
## Mitoses7

## # weights:  247
## initial  value 78.032179 
## iter  10 value 7.503883
## iter  20 value 1.448965
## iter  30 value 1.386272
## iter  40 value 0.005642
## iter  50 value 0.002634
## iter  60 value 0.001669
## iter  70 value 0.001376
## iter  80 value 0.000380
## iter  90 value 0.000289
## iter 100 value 0.000171
## final  value 0.000171 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses5,
## Mitoses7

## # weights:  411
## initial  value 77.106773 
## iter  10 value 1.573781
## iter  20 value 0.065635
## iter  30 value 0.014075
## iter  40 value 0.003513
## iter  50 value 0.000699
## iter  60 value 0.000277
## final  value 0.000088 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses5,
## Mitoses7

## # weights:  83
## initial  value 90.018263 
## iter  10 value 19.128137
## iter  20 value 12.063274
## iter  30 value 10.666813
## iter  40 value 10.615723
## iter  50 value 10.615513
## final  value 10.615513 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses5,
## Mitoses7

## # weights:  247
## initial  value 88.487395 
## iter  10 value 17.253317
## iter  20 value 9.056591
## iter  30 value 5.535172
## iter  40 value 5.371946
## iter  50 value 5.371081
## final  value 5.371064 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses5,
## Mitoses7

## # weights:  411
## initial  value 134.047989 
## iter  10 value 13.205610
## iter  20 value 5.081229
## iter  30 value 4.491152
## iter  40 value 4.465657
## iter  50 value 4.465407
## final  value 4.465402 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses5,
## Mitoses7

## # weights:  83
## initial  value 78.555308 
## iter  10 value 13.760224
## iter  20 value 8.202709
## iter  30 value 8.177486
## iter  40 value 8.176874
## iter  50 value 8.162793
## iter  60 value 7.464108
## iter  70 value 4.889542
## iter  80 value 0.634184
## iter  90 value 0.130710
## iter 100 value 0.082904
## final  value 0.082904 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses5,
## Mitoses7

## # weights:  247
## initial  value 110.338405 
## iter  10 value 11.148513
## iter  20 value 8.637958
## iter  30 value 8.253434
## iter  40 value 4.878137
## iter  50 value 4.861398
## iter  60 value 0.911550
## iter  70 value 0.094895
## iter  80 value 0.085611
## iter  90 value 0.070217
## iter 100 value 0.065438
## final  value 0.065438 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses5,
## Mitoses7

## # weights:  411
## initial  value 109.861894 
## iter  10 value 13.011499
## iter  20 value 7.993956
## iter  30 value 5.203492
## iter  40 value 5.147781
## iter  50 value 4.513468
## iter  60 value 3.010592
## iter  70 value 0.301052
## iter  80 value 0.162978
## iter  90 value 0.153303
## iter 100 value 0.140041
## final  value 0.140041 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  83
## initial  value 79.556502 
## iter  10 value 6.515499
## iter  20 value 0.392009
## iter  30 value 0.025637
## iter  40 value 0.002196
## iter  50 value 0.000553
## iter  60 value 0.000130
## iter  60 value 0.000072
## iter  60 value 0.000072
## final  value 0.000072 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  247
## initial  value 89.990310 
## iter  10 value 2.388830
## iter  20 value 1.913248
## iter  30 value 1.390752
## iter  40 value 1.386772
## iter  50 value 1.386640
## iter  60 value 1.386517
## iter  70 value 1.386321
## final  value 1.386294 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  411
## initial  value 120.171277 
## iter  10 value 2.318178
## iter  20 value 2.262596
## iter  30 value 2.249363
## iter  40 value 2.014047
## iter  50 value 1.909734
## final  value 1.909587 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  83
## initial  value 81.505147 
## iter  10 value 24.936676
## iter  20 value 10.408844
## iter  30 value 10.171230
## iter  40 value 10.163501
## final  value 10.163501 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  247
## initial  value 77.835027 
## iter  10 value 13.539075
## iter  20 value 6.637977
## iter  30 value 5.619076
## iter  40 value 5.368213
## final  value 5.368091 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  411
## initial  value 98.768147 
## iter  10 value 6.263095
## iter  20 value 4.518790
## iter  30 value 4.452266
## iter  40 value 4.450987
## final  value 4.450986 
## converged

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  83
## initial  value 80.164292 
## iter  10 value 22.809665
## iter  20 value 10.184019
## iter  30 value 10.164862
## iter  40 value 10.157933
## iter  50 value 10.142994
## iter  60 value 4.847206
## iter  70 value 0.163345
## iter  80 value 0.070804
## iter  90 value 0.064345
## iter 100 value 0.057228
## final  value 0.057228 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  247
## initial  value 116.007248 
## iter  10 value 4.923078
## iter  20 value 4.779633
## iter  30 value 4.338244
## iter  40 value 2.861976
## iter  50 value 2.550241
## iter  60 value 2.369424
## iter  70 value 1.495746
## iter  80 value 0.220865
## iter  90 value 0.150654
## iter 100 value 0.135248
## final  value 0.135248 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  411
## initial  value 106.521447 
## iter  10 value 0.410637
## iter  20 value 0.296084
## iter  30 value 0.270696
## iter  40 value 0.170487
## iter  50 value 0.141572
## iter  60 value 0.120177
## iter  70 value 0.097221
## iter  80 value 0.066111
## iter  90 value 0.057062
## iter 100 value 0.049191
## final  value 0.049191 
## stopped after 100 iterations

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## # weights:  83
## initial  value 88.087835 
## iter  10 value 33.004046
## iter  20 value 16.844765
## iter  30 value 11.852408
## iter  40 value 11.094320
## iter  50 value 11.044422
## iter  60 value 11.042636
## iter  70 value 11.042410
## iter  80 value 11.042389
## final  value 11.042387 
## converged

resultado_entrenamiento5 <- predict(modelo5, entrenamiento)
resultado_prueba5 <- predict(modelo5, prueba)

#Matriz de Consufión 
mcre5 <- confusionMatrix(resultado_entrenamiento5,entrenamiento$Class) #Matriz de confusion de resultado de entrenamiento
mcre5

## Confusion Matrix and Statistics
## 
##            Reference
## Prediction  benign malignant
##   benign        88         0
##   malignant      0        47
##                                     
##                Accuracy : 1         
##                  95% CI : (0.973, 1)
##     No Information Rate : 0.6519    
##     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 : 0.6519    
##          Detection Rate : 0.6519    
##    Detection Prevalence : 0.6519    
##       Balanced Accuracy : 1.0000    
##                                     
##        'Positive' Class : benign    
## 

mcrp5 <- confusionMatrix(resultado_prueba5, prueba$Class)  #Matriz de confusion de resultado de prueba
mcrp5

## Confusion Matrix and Statistics
## 
##            Reference
## Prediction  benign malignant
##   benign       344        11
##   malignant     12       181
##                                           
##                Accuracy : 0.958           
##                  95% CI : (0.9377, 0.9732)
##     No Information Rate : 0.6496          
##     P-Value [Acc > NIR] : <2e-16          
##                                           
##                   Kappa : 0.9079          
##                                           
##  Mcnemar's Test P-Value : 1               
##                                           
##             Sensitivity : 0.9663          
##             Specificity : 0.9427          
##          Pos Pred Value : 0.9690          
##          Neg Pred Value : 0.9378          
##              Prevalence : 0.6496          
##          Detection Rate : 0.6277          
##    Detection Prevalence : 0.6478          
##       Balanced Accuracy : 0.9545          
##                                           
##        'Positive' Class : benign          
## 

6. Modelo con el método rf

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))
                )

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7,
## Mitoses8

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7,
## Mitoses8

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7,
## Mitoses8

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses5,
## Mitoses6, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses5,
## Mitoses6, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses5,
## Mitoses6, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bare.nuclei9, Bl.cromatin9,
## Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bare.nuclei9, Bl.cromatin9,
## Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bare.nuclei9, Bl.cromatin9,
## Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bl.cromatin9, Mitoses4, Mitoses7

resultado_entrenamiento6 <- predict(modelo6, entrenamiento)
resultado_prueba6 <- predict(modelo6, prueba)

#Matriz de Consufión 
mcre6 <- confusionMatrix(resultado_entrenamiento6,entrenamiento$Class) #Matriz de confusion de resultado de entrenamiento
mcre6

## Confusion Matrix and Statistics
## 
##            Reference
## Prediction  benign malignant
##   benign        88         0
##   malignant      0        47
##                                     
##                Accuracy : 1         
##                  95% CI : (0.973, 1)
##     No Information Rate : 0.6519    
##     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 : 0.6519    
##          Detection Rate : 0.6519    
##    Detection Prevalence : 0.6519    
##       Balanced Accuracy : 1.0000    
##                                     
##        'Positive' Class : benign    
## 

mcrp6 <- confusionMatrix(resultado_prueba6, prueba$Class)  #Matriz de confusion de resultado de prueba
mcrp6

## Confusion Matrix and Statistics
## 
##            Reference
## Prediction  benign malignant
##   benign       345        12
##   malignant     11       180
##                                           
##                Accuracy : 0.958           
##                  95% CI : (0.9377, 0.9732)
##     No Information Rate : 0.6496          
##     P-Value [Acc > NIR] : <2e-16          
##                                           
##                   Kappa : 0.9077          
##                                           
##  Mcnemar's Test P-Value : 1               
##                                           
##             Sensitivity : 0.9691          
##             Specificity : 0.9375          
##          Pos Pred Value : 0.9664          
##          Neg Pred Value : 0.9424          
##              Prevalence : 0.6496          
##          Detection Rate : 0.6296          
##    Detection Prevalence : 0.6515          
##       Balanced Accuracy : 0.9533          
##                                           
##        'Positive' Class : benign          
## 

Resumen de resultados

resultados <- data.frame(
  "svmLinear" = c(mcre1$overall["Accuracy"], mcrp1$overall["Accuracy"]),
  "svmRadial" = c(mcre2$overall["Accuracy"], mcrp2$overall["Accuracy"]),
  "svmPoly" = c(mcre3$overall["Accuracy"], mcrp3$overall["Accuracy"]),
  "rpart" = c(mcre4$overall["Accuracy"], mcrp4$overall["Accuracy"]),
  "nnet" = c(mcre5$overall["Accuracy"], mcrp5$overall["Accuracy"]),
  "rf" = c(mcre6$overall["Accuracy"], mcrp6$overall["Accuracy"])
)
rownames(resultados) <- c("Precision de entrenamiento", "Precision de prueba")
resultados

##                            svmLinear svmRadial   svmPoly     rpart      nnet
## Precision de entrenamiento 1.0000000  1.000000 1.0000000 0.9407407 1.0000000
## Precision de prueba        0.9580292  0.649635 0.9580292 0.9270073 0.9580292
##                                   rf
## Precision de entrenamiento 1.0000000
## Precision de prueba        0.9580292