subtopic

Train / validation / test and overfitting

Learn why we keep data aside: train (learn), validation (tune and stop), test (final score, used once). Read learning curves: training loss down but validation loss up = overfitting.

Builds on: M01 · Build: What Machine Learning Is (rules vs learning), M03 · Build: Model-Ready Data & Baselines