ML Methodology · Activity 3

Cross-Validation Microscope

Step through the folds one at a time. See exactly which examples train the model, which examples evaluate it, and why k-fold CV uses data more efficiently than a single holdout.

Predict first: As k increases from 3-fold toward leave-one-out, what happens to the number of training examples used in each fit, and what happens to the number of models we must train?

Current fold

Used for fitting in this foldHeld out in this foldModel fitted without held-out points

Fold errors

Fold# train# held outMSE