r - Predictive model decision tree -


i want build predictive model using decision tree classification in r. used code:

library(rpart) library(caret) datayesno <- read.csv('datayesno.csv', header=t) summary(datayesno) worktrain <- sample(1:50, 40) worktest  <- setdiff(1:50, worktrain) datayesno[worktrain,] datayesno[worktest,] m      <- ncol(datayesno) input  <- names(datayesno)[1:(m-1)]                  target <- “yesno”                                        tree   <- rpart(yesno~var1+var2+var3+var4+var5,                 data=datayesno[worktrain, c(input,target)],                 method="class",                 parms=list(split="information"),                 control=rpart.control(usesurrogate=0, maxsurrogate=0))  summary(tree)  plot(tree) text(tree)

i got 1 root (var3) , 2 leafs (yes, no). i'm not sure result. how can confusion matrix, accuracy, sensitivity, , specificity? can them caret package?

if use model make predictions on test set, can use confusionmatrix() measures you're looking for.

something this...

predictions <- predict(tree, worktest) cmatrix <- confusionmatrix(predictions, worktest$yesno) print(cmatrix)

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