How to do train, validation and test using Caret package in R?

train allows you to do validation and much more. You can supply a trainControl function to the trControl argument that allows you to specify the details of your training procedure. By default train already splits 75% of the data you pass into it for training and 25% for validation, you can also change this in the trainControl.

I suggest you check out train and trainControl documentation, here and here to know more about the details you can specify in your training procedure.

Below is a simple example of training a Random Forest with 5-Fold Cross Validation and doing standardization of the data using Caret and the train function to better exemplify.

(Note: I added some unecessary things such as verboseIter = TRUE and classProbs = TRUE just to show you some features you get with caret)

library(caret)
library(datasets)

# Loading the iris dataset
data(iris)

# Specifying an 80-20 train-test split
train_idx = createDataPartition(iris$Species, p = .8, list = F)

# Creating the training and testing sets
train = iris[train_idx, ]
test = iris[-train_idx, ]

# Declaring the trainControl function
train_ctrl = trainControl(
  method  = "cv", #Specifying Cross validation
  number  = 5, # Specifying 5-fold
  verboseIter = TRUE, # So that each iteration you get an update of the progress
  classProbs = TRUE # So that you can obtain the probabilities for each example
)

rf_model = train(
  Species ~., # Specifying the response variable and the feature variables
  method = "rf", # Specifying the model to use
  data = train, 
  trControl = train_ctrl,
  preProcess = c("center", "scale") # Do standardization of the data
)

# Get the predictions of your model in the test set
predictions = predict(rf_model, newdata = test)

# See the confusion matrix of your model in the test set
confusionMatrix(predictions, test$Species)

Hope this helps you a bit