Reputation: 433
I wanted to split my training data in to 70% training, 15% testing and 15% validation. I am using the createDataPartition()
function of the caret package. I am splitting it like the following
train <- read.csv("Train.csv")
test <- read.csv("Test.csv")
split=0.70
trainIndex <- createDataPartition(train$age, p=split, list=FALSE)
data_train <- train[ trainIndex,]
data_test <- train[-trainIndex,]
Is there any way of splitting into training, testing and validation using createDataPartition()
like the following H2o
approach?
data.hex <- h2o.importFile("Train.csv")
splits <- h2o.splitFrame(data.hex, c(0.7,0.15), destination_frames = c("train","valid","test"))
train.hex <- splits[[1]]
valid.hex <- splits[[2]]
test.hex <- splits[[3]]
Upvotes: 4
Views: 19980
Reputation: 379
Take a look at train,validation, test split model in CARET in R. The idea is to use createDataPartition() twice. First time p=0.7 to create 70% train and 30% remaining data. Second time p=0.5 on remaining data to create 15% testing and 15% validate.
Upvotes: 0
Reputation: 38520
A method using the sample()
function in base R is
splitSample <- sample(1:3, size=nrow(data.hex), prob=c(0.7,0.15,0.15), replace = TRUE)
train.hex <- data.hex[splitSample==1,]
valid.hex <- data.hex[splitSample==2,]
test.hex <- data.hex[splitSample==3,]
Upvotes: 9