When should I use cross-validation?

What is cross-validation and why is it important?

Cross-validation is a technique used to assess how a model will generalize to an independent dataset. It's important because it helps prevent overfitting and ensures that the model performs well on unseen data.

When should I use cross-validation in my projects?

You should use cross-validation when you have a limited dataset, want to evaluate model performance, or need to tune hyperparameters.

Are there different types of cross-validation?

Yes, common types include k-fold cross-validation, stratified k-fold, and leave-one-out cross-validation. Each has its use cases.

How do I implement cross-validation in Python?

You can use libraries like scikit-learn, which provide functions like cross_val_score to easily implement cross-validation.