Groups
Double descent describes how test error first follows the classic U-shape with increasing model complexity, spikes near the interpolation threshold, and then drops again in the highly overparameterized regime.
The bias–variance tradeoff explains how prediction error splits into bias squared, variance, and irreducible noise for squared loss.