J48 tree in R - train and test classification

Question

I want to use train and test in J48 decision-tree on R. here is my code:

library("RWeka")

data <- read.csv("try.csv")
resultJ48 <- J48(classificationTry~., data)

summary(resultJ48)

but I want to split my data into 70% train and 30% test, how can I use the J48 algo to do it?

many thanks!

Answer 1

use the sample.split() function of the caTools package. It is more leightweight than the caret package (which is a meta package if I remember correctly):

library(caTools)

library(RWeka)

data <- read.csv("try.csv")
spl = sample.split(data$someAttribute, SplitRatio = 0.7)

dataTrain = subset(data, spl==TRUE)
dataTest = subset(data, spl==FALSE)

resultJ48 <- J48(as.factor(classAttribute)~., dataTrain) 
dataTest.pred <- predict(resultJ48, newdata = dataTest)
table(dataTest$classAttribute, dataTest.pred)

Answer 2

It is not in R. But in java... But you will understand the logic with it.

int trainSize = (int) Math.round(trainingSet.numInstances() * 0.7); //70% split 
int testSize = trainingSet.numInstances() - trainSize;
Instances train = new Instances(trainingSet, 0, trainSize);
Instances test = new Instances(trainingSet, trainSize, testSize)

Implement in R with same logic. Hope it helps :)

Answer 3

You may want to check the createDataPartition in caret package.

Answer 4

If you don't want to use more packages other than RWeka, you can do it with runif:

library("RWeka")
data <- read.csv("try.csv")

randoms=runif(nrow(data))

resultJ48 <- J48(classificationTry~., data[randoms<=0.7,])
PredTest <- predict(resultJ48, newdata = data[randoms>0.7,])
table(data[randoms>0.7,]$classificationTry, PredTest)