How do I choose the right cross-validation technique?

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

Choosing the right cross-validation technique depends on your dataset size and model type. Are you working with a small or large dataset?

I have a small dataset, what should I use?

For small datasets, k-fold cross-validation is often recommended. It helps in maximizing the training data while providing a reliable estimate of model performance.

What if I have a large dataset?

With large datasets, you might consider using stratified k-fold or leave-one-out cross-validation, as they can provide more robust estimates without excessive computation.

Are there any specific scenarios where I should avoid certain techniques?

Yes, avoid techniques like leave-one-out cross-validation if your dataset is very large, as it can be computationally expensive and time-consuming.