Training, Validation & Model Selection

Model Comparison Lab

Compare model families fairly on the same prepared data, rank by validation performance and reserve the test set for the selected winner.

14 · Pipeline 15 · Compare 16 · Tune 17 · Cross-validation 18 · Bias/variance 19 · History

Lab concept guide

What to observe while you experiment

Fair model comparison keeps the dataset, preprocessing boundary, folds and metric constant while changing the model family/configuration. The goal is evidence for selection, not a leaderboard built from incomparable experiments.

MechanismEvaluate each candidate on the same validation folds and rank only after all candidates have been scored under the same protocol.

Failure modeComparing a tuned model with an untuned baseline on different splits or selecting the winner after inspecting test performance.

VerificationConfirm identical fold assignments and metric definitions across candidates, then evaluate only the selected winner on the untouched test set.

Experiment deliberately

Compare at least three model families on fixed folds. Predict which may gain from nonlinear structure, then explain the ranking and fold variability.

Model-selection rule:Models are ranked on the validation set. The final test score is reported only for the validation winner, avoiding test-set shopping.

DatasetNoise0.28Seed

Ready.

Validation ranking

Six model families trained on exactly the same training split.

Validation evidence

F1 and balanced accuracy are shown side by side.

Winner separator simulation

The observations stay fixed while the validation winner’s separator is revealed.

Separator modelChoose any compared classifier to inspect its separator.