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

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

K-fold validation divides the dataset into k subsets, using k-1 for training and 1 for testing, rotating through all subsets. Holdout validation splits the dataset into two parts, typically a training set and a test set, without rotation.

Which method is better for model evaluation?

It depends on the dataset size and the model. K-fold is generally better for smaller datasets as it uses more data for training, while holdout is simpler and faster for larger datasets.

Can you explain how to implement k-fold validation?

Sure! You can use libraries like scikit-learn in Python. You create a KFold object, then loop through the splits to train and test your model.

What are the advantages of using holdout validation?

Holdout validation is faster and easier to implement, making it suitable for large datasets where cross-validation might be computationally expensive.