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

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

K-fold cross-validation divides the dataset into k subsets, using each subset for testing while training on the others. Holdout, on the other hand, splits the dataset into two parts: one for training and one for testing.

Which method is better for model evaluation?

K-fold cross-validation is generally better for model evaluation as it provides a more reliable estimate of model performance by using multiple training and testing sets.

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

Yes, k-fold cross-validation can be computationally expensive, especially with large datasets, as it requires training the model k times.

When should I use holdout instead?

Holdout is suitable for large datasets where computational efficiency is a concern, or when you need a quick evaluation without extensive training.