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 data size, model type, and the problem you're solving. What type of model are you working with?

I'm working with a classification model and I have a moderate dataset.

For a classification model with a moderate dataset, techniques like k-fold cross-validation or stratified sampling can be effective. Have you considered these?

I haven't. Can you explain how k-fold cross-validation works?

Sure! 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 subset. 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. Larger datasets can use a higher k, while smaller ones might benefit from a lower k to ensure enough data for training.