What is the difference between k-fold cross-validation and other validation methods?

What is the difference between k-fold cross-validation and other validation methods?

K-fold cross-validation is a technique used to assess the performance of a model by dividing the dataset into k subsets. Unlike methods like holdout validation, which uses a single train-test split, k-fold ensures that every data point gets to be in both training and testing sets across different iterations.

Why is k-fold preferred over simple train-test splits?

K-fold is preferred because it reduces variability in the model's performance estimate. By using multiple splits, it provides a more reliable measure of how the model will perform on unseen data.

Are there any downsides to using k-fold cross-validation?

Yes, k-fold can be computationally expensive, especially with large datasets, as it requires training the model k times. Additionally, if k is too large, it may lead to overfitting.

What are some alternatives to k-fold cross-validation?

Alternatives include leave-one-out cross-validation (LOOCV), stratified k-fold, and simple holdout methods. Each has its own advantages depending on the dataset and problem.