What is the difference between k-fold validation and train-test split?

What is the difference between k-fold validation and train-test split?

K-fold validation divides the dataset into k subsets, using k-1 for training and 1 for testing, rotating through all subsets. Train-test split, on the other hand, simply divides the dataset into two parts: one for training and one for testing.

When should I use k-fold validation instead of train-test split?

Use k-fold validation when you want a more reliable estimate of model performance, especially with smaller datasets. It helps reduce variance in the evaluation.

Are there any downsides to using k-fold validation?

Yes, k-fold validation can be computationally expensive, as it requires training the model k times. It may also lead to overfitting if not done carefully.

Can you give an example of how to implement k-fold validation?

Sure! In Python, you can use libraries like scikit-learn. You would use KFold from sklearn.model_selection to create the folds and then iterate through them to train and test your model.