What are the best practices for cross-validation in machine learning?

What are the best practices for cross-validation in machine learning?

Some best practices include using k-fold cross-validation, stratifying your folds, and ensuring your data is shuffled.

Why is stratification important in cross-validation?

Stratification ensures that each fold has a representative distribution of the target variable, which helps in better model evaluation.

How do I choose the right value for k in k-fold cross-validation?

A common practice is to use k=5 or k=10, but it can depend on your dataset size and the trade-off between bias and variance.

Can cross-validation help prevent overfitting?

Yes, cross-validation helps in assessing how the results of a statistical analysis will generalize to an independent dataset, thus reducing overfitting.

What are some common pitfalls to avoid in cross-validation?

Common pitfalls include data leakage, not shuffling data, and using the same data for training and validation.