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

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

K-fold cross-validation divides the dataset into k subsets, using each subset for testing while training on the others. This contrasts with techniques like holdout validation, where the data is split into just two sets.

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

K-fold provides a more reliable estimate of model performance by reducing variance, as it uses multiple train-test splits.

Are there any downsides to using k-fold validation?

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

What are some other validation techniques I should know about?

Other techniques include stratified k-fold, leave-one-out cross-validation, and time series split, each suited for different scenarios.

When should I use stratified k-fold instead of regular k-fold?

Stratified k-fold is ideal when you have imbalanced classes, as it ensures each fold has a representative distribution of classes.