Example

library(caret)

## Loading required package: lattice
## Loading required package: ggplot2

data(iris)
library(ggplot2)
table(iris$Species)

## 
##     setosa versicolor  virginica 
##         50         50         50

Create training and test set

intrain<- createDataPartition(y=iris$Species,p=0.7,list=F)
training<- iris[intrain,]
testing<- iris[-intrain,]
dim(training)

## [1] 105   5

dim(testing)

## [1] 45  5

Irir petal withs/Sepal width

qplot(Petal.Width,Sepal.Width,colour=Species,data=training)

#You can find out the 3 cluster 

Irir petal widths/sepal width

library(caret)
modFit<- train(Species~.,method="rpart",data=training)

## Loading required package: rpart

#rpart is a tree method in caret package
print(modFit$finalModel)

## n= 105 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 105 70 setosa (0.3333333 0.3333333 0.3333333)  
##   2) Petal.Length< 2.45 35  0 setosa (1.0000000 0.0000000 0.0000000) *
##   3) Petal.Length>=2.45 70 35 versicolor (0.0000000 0.5000000 0.5000000)  
##     6) Petal.Length< 4.75 31  0 versicolor (0.0000000 1.0000000 0.0000000) *
##     7) Petal.Length>=4.75 39  4 virginica (0.0000000 0.1025641 0.8974359) *

#You can find 3 node are significant

Plot tree

plot(modFit$finalModel,uniform=T,main="Classification Tree")
text(modFit$finalModel,use.n=T,all=T,cex=0.8)

#Prettier plots

library(rattle)

## Rattle: A free graphical interface for data mining with R.
## Version 3.4.1 Copyright (c) 2006-2014 Togaware Pty Ltd.
## Type 'rattle()' to shake, rattle, and roll your data.

fancyRpartPlot(modFit$finalModel)

#Predicting new values

predict(modFit,newdata=testing)

##  [1] setosa     setosa     setosa     setosa     setosa     setosa    
##  [7] setosa     setosa     setosa     setosa     setosa     setosa    
## [13] setosa     setosa     setosa     versicolor versicolor versicolor
## [19] versicolor versicolor versicolor versicolor versicolor versicolor
## [25] virginica  virginica  versicolor versicolor versicolor versicolor
## [31] virginica  virginica  virginica  versicolor virginica  virginica 
## [37] virginica  virginica  virginica  virginica  virginica  virginica 
## [43] virginica  virginica  virginica 
## Levels: setosa versicolor virginica