What are some common cross-validation techniques in machine learning?

What are some common cross-validation techniques in machine learning?

Common cross-validation techniques include k-fold, stratified k-fold, leave-one-out, and repeated cross-validation.

Can you explain how k-fold cross-validation works?

Sure! In k-fold cross-validation, the dataset is divided into k subsets. The model is trained on k-1 subsets and tested on the remaining subset. This process is repeated k times, with each subset used as the test set once.

What is the advantage of using stratified k-fold?

Stratified k-fold ensures that each fold has a representative distribution of the target variable, which is especially useful for imbalanced datasets.

Are there any drawbacks to cross-validation?

Yes, cross-validation can be computationally expensive, especially with large datasets, and it may not always capture the model's performance on unseen data.