Overfitting Arena
Build intuition for train error, test error, and the generalization gap. Change model flexibility, sample count, and label noise, then watch when the fit is too simple, just right, or memorizing the sample.
Overfitting Arena
Tune model flexibility, training set size, and label noise. Watch when the fit captures signal, when it crawls, and when it starts memorizing noise.
Plenty of clean points. Easy to see the sweet spot before the model starts memorizing wiggles.
Underfitting
The model is too stiff to capture the pattern, so both training and hidden-test error stay high.
Increase flexibility a few notches before touching anything else.
Hidden test set is hidden. Reveal it after you make a prediction.
Training loss usually falls monotonically. Test error only improves until the model starts memorizing.
Built by Robby Sneiderman