How do I choose the right validation technique for my model?

How do I choose the right validation technique for my model?

Choosing the right validation technique depends on your dataset size, model type, and the problem you're solving. Are you working with a small or large dataset?

I have a small dataset, around 100 samples.

For a small dataset, techniques like k-fold cross-validation or leave-one-out cross-validation can be effective. Have you considered these?

I've heard of k-fold, but I'm not sure how it works.

K-fold cross-validation involves splitting your dataset into 'k' subsets. You train your model on 'k-1' subsets and validate it on the remaining one. This process is repeated 'k' times, ensuring each subset is used for validation once.

That sounds useful! How do I decide the value of k?

A common choice for k is 5 or 10, but it can depend on your dataset size. For smaller datasets, a higher k can provide better estimates, but it also increases computation time.