When should I use cross-validation in machine learning?

When should I use cross-validation in machine learning?

Cross-validation is essential when you want to assess the performance of your model on unseen data. It helps in avoiding overfitting.

Can you explain the different types of cross-validation?

Sure! The most common types are k-fold, stratified k-fold, and leave-one-out cross-validation. Each has its own use cases.

How do I choose the right type of cross-validation for my dataset?

It depends on your dataset size and the problem type. For smaller datasets, leave-one-out might be useful, while k-fold is great for larger datasets.

What are the drawbacks of using cross-validation?

Cross-validation can be computationally expensive and time-consuming, especially with large datasets and complex models.