ML: BreastCancer
Daniel Nájera - A01709578
2024-02-29
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
#install.packages("caret") #Algoritmos de aprendizaje
library(caret)## Loading required package: ggplot2## Loading required package: lattice#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)Crear base de datos
df <- data(BreastCancer)
#install.packages("mlbench")
library(mlbench)
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.3 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ lubridate 1.9.2 ✔ tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ purrr::lift() masks caret::lift()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsdf <- 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): 17str(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, Mitoses7resultado_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
## convergedresultado_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, Mitoses7resultado_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 0.9580292Conclusiones
El modelo con el método de bosques aleatorios presenta sobreajuste, ya que tiene una alta precisión en entrenamiento, pero baja en prueba. Acorde al resumen de resultados, el mejor modelo es el de Máquina de vectores de soporte lineal; que a pesar de quedar empata con el polinómico se eligió este por ser más sencillo.
---
title: 'ML: BreastCancer'
author: "Daniel Nájera - A01709578"
date: "2024-02-29"
output: 
  html_document: 
    toc: TRUE
    toc_float: TRUE
    code_download: TRUE
---

![](/Users/danielnajera/Downloads/ML.png)

# 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
```{r}
#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)
```

# Crear base de datos
```{r}
df <- data(BreastCancer)
#install.packages("mlbench")
library(mlbench)
library(tidyverse)
df <- data.frame(BreastCancer)
df <- df %>% select(-Id)
#View(df)
```

#Análisis exploratorio
```{r}
summary(df)
str(df)
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
```{r}
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  
```{r}
#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
                )

resultado_entrenamiento1 <- predict(modelo1, entrenamiento)

resultado_prueba1 <- predict(modelo1, prueba)

#Matriz de Consufión 
mcre1 <- confusionMatrix(resultado_entrenamiento1,entrenamiento$Class) #Matriz de confusion de resultado de entrenamiento
mcre1
mcrp1 <- confusionMatrix(resultado_prueba1, prueba$Class)  #Matriz de confusion de resultado de prueba
mcrp1
```

# 2. Modelo con el método svmRadial  
```{r}
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
                )

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

#Matriz de Consufión 
mcre2 <- confusionMatrix(resultado_entrenamiento2,entrenamiento$Class) #Matriz de confusion de resultado de entrenamiento
mcre2
mcrp2 <- confusionMatrix(resultado_prueba2, prueba$Class)  #Matriz de confusion de resultado de prueba
mcrp2
```

# 3. Modelo con el método svmPoly
```{r}
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

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

#Matriz de Consufión 
mcre3 <- confusionMatrix(resultado_entrenamiento3,entrenamiento$Class) #Matriz de confusion de resultado de entrenamiento
mcre3
mcrp3 <- confusionMatrix(resultado_prueba3, prueba$Class)  #Matriz de confusion de resultado de prueba
mcrp3
```
# 4. Modelo con el método rpart
```{r}
modelo4 <- train(Class ~ ., data=entrenamiento, 
                method = "rpart", 
                preProcess = c("scale", "center"),
                trControl = trainControl(method="cv", number=10),
                tuneLength = 10 #Cambiar
                )

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

#Matriz de Consufión 
mcre4 <- confusionMatrix(resultado_entrenamiento4,entrenamiento$Class) #Matriz de confusion de resultado de entrenamiento
mcre4
mcrp4 <- confusionMatrix(resultado_prueba4, prueba$Class)  #Matriz de confusion de resultado de prueba
mcrp4
```

# 5. Modelo con el metodo nnet
```{r}
modelo5 <- train(Class ~ ., data=entrenamiento, 
                method = "nnet", 
                preProcess = c("scale", "center"),
                trControl = trainControl(method="cv", number=10)
                )

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

#Matriz de Consufión 
mcre5 <- confusionMatrix(resultado_entrenamiento5,entrenamiento$Class) #Matriz de confusion de resultado de entrenamiento
mcre5
mcrp5 <- confusionMatrix(resultado_prueba5, prueba$Class)  #Matriz de confusion de resultado de prueba
mcrp5
```

# 6. Modelo con el método rf
```{r}
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))
                )

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

#Matriz de Consufión 
mcre6 <- confusionMatrix(resultado_entrenamiento6,entrenamiento$Class) #Matriz de confusion de resultado de entrenamiento
mcre6
mcrp6 <- confusionMatrix(resultado_prueba6, prueba$Class)  #Matriz de confusion de resultado de prueba
mcrp6
```

# Resumen de resultados
```{r}
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
```

# Conclusiones
El modelo con el método de bosques aleatorios presenta sobreajuste, ya que tiene una alta precisión en entrenamiento, pero baja en prueba.
Acorde al resumen de resultados, el mejor modelo es el de **Máquina de vectores de soporte lineal**; que a pesar de quedar empata con el polinómico se eligió este por ser más sencillo.