What is cross-validation in AI?

What is cross-validation in AI?

Cross-validation is a technique used to assess how the results of a statistical analysis will generalize to an independent dataset.

Why is cross-validation important?

It's important because it helps to prevent overfitting, ensuring that the model performs well on unseen data.

Can you explain the different types of cross-validation?

Sure! The most common types are k-fold, stratified k-fold, leave-one-out, and holdout cross-validation.

How do I implement k-fold cross-validation?

You can implement k-fold cross-validation by splitting your dataset into k subsets, training on k-1 subsets, and validating on the remaining subset.

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

Common pitfalls include using the same data for training and validation, not shuffling data, and choosing an inappropriate k value.