Machine Learning Fundamentals · Model evaluation

Cross-validation

Splitting the data multiple different ways and averaging performance, so one lucky/unlucky split doesn't mislead you.

Also known as: k-fold cross-validation.

Start a lesson on Cross-validation

Learn first

• Train/test/validation split: Holding out data the model never trains on, so you can honestly check how it does on unseen examples.

Leads to

• Hyperparameter tuning: Systematically searching for the settings (learning rate, regularization strength, tree depth) that aren't learned from data but chosen before training.

Related topics

• Hyperparameter tuning: Systematically searching for the settings (learning rate, regularization strength, tree depth) that aren't learned from data but chosen before training.