In this tutorial, you will learn how to split sample into training and test data sets with R.

The following code splits 70% of the data selected randomly into training set and the remaining 30% sample into test data set.

Here

In the above program,

The following code splits 70% of the data selected randomly into training set and the remaining 30% sample into test data set.

data<-read.csv("c:/datafile.csv")

dt = sort(sample(nrow(data), nrow(data)*.7))

train<-data[dt,]

test<-data[-dt,]

Here

**sample( )**function randomly picks 70% rows from the data set. It is sampling without replacement.**Method 2 :**To maintain same percentage of event rate in both training and validation dataset.library(caret)

set.seed(3456)

trainIndex <- createDataPartition(data$FD, p = .7,

list = FALSE,

times = 1)

Train <- data[ trainIndex,]

Valid <- data[-trainIndex,]

In the above program,

**FD**is a dependent variable having two values 1 and 0.**Make sure it is defined in factor format.**
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ReplyDeleteThis won't randomize the order, bad option

ReplyDeleteWhat do you mean by "This won't randomize the order"? Sample function randomize the order.

Deletehow can i select specific data i don't want to randomize data

DeleteOne more way to split data into two part

ReplyDeleteinstall.packages("caTools")

library(caTools)

iris <- iris

iris$spl <-sample.split(iris,SplitRatio = 0.7)

head(iris)

train=subset(iris, iris$spl==TRUE)

test <- subset(iris,iris$spl ==FALSE)

This is the correct solution when you have a dependent column. Thank you, Rishabh.

DeleteIt helped me a lot.Thank you

ReplyDeleteVery helpful and to the point, thanks Deepanshu!

ReplyDelete