cross-validation
2 free lessons tagged cross-validation across AI. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
Generalization: Measuring What a Model Actually Learned
Training error flatters every model. Learn how train, validation and test splits work, watch overfitting happen in computed numbers, and see how regularization, cross-validation and leak-proof pipelines give you a score you can trust on data the model has never seen.
Validation: Measuring Anything at All
Shuffled cross-validation reports 54 percent accuracy on data containing no signal, because neighbouring samples share their futures. This lesson builds purging, embargo and sample uniqueness weights, shows the illusion scaling with label overlap and vanishing when the fix is applied, and covers why a single walk-forward path is weak evidence.