# in-sample data vs out-of-sample data

I know that a train-validation-test splits the data into:

• a training dataset - obviously my "in-sample" data
• a validation dataset
• a test data set - obviously my "out-of-sample" data

My question is: Should I refer to the validation dataset as in-sample or out-of-sample data?

If we're using the validation dataset to fine-tune the parameter values, then the model has seen this data before. So I'm thinking it is "in-sample" data. Am I right?

Kitty Kenty.

Generally, the train data is then split in $$n$$ parts. $$n-1$$ of them are used for training and remaining $$1$$ is used for validation. And, this process is repeated until all the $$n$$ parts become validation sets once.
So, yes, validation data is your in-sample data