###############################
require(caret)
setwd("F:\\DataMining_R\\3_LectureData\\section9")

tumor=read.csv("cancer_tumor.csv") #predict if the tumor is
#malignant (M) or benign (B)

head(tumor)

#drop columns
drops =c("id","X")
df=tumor[ , !(names(tumor) %in% drops)]

head(df)

table(df$diagnosis)

prop.table(table(df$diagnosis))

fitd= train(diagnosis~., data=df, method="rpart",
            trControl=train_control)

fitd

fancyRpartPlot(fitd$finalModel)

summary(fitd)
