benign-overfitting-overparameterization

IN premise — entries/2026/06/21/wiki-Overfitting-chunk-2.md

Created 2026-06-21T09:55:51+00:00

Benign overfitting — where a model perfectly fits noisy training data yet still generalizes well — requires overparameterization where the number of unimportant directions in parameter space significantly exceeds the sample size.

Dependents

These beliefs depend on this one:

• IN classical-generalization-theory-overturned — Classical generalization theory — the U-shaped bias-variance tradeoff — has been overturned by two empirical phenomena: double descent shows test error decreasing again far past the interpolation threshold, and benign overfitting shows perfect training fit coexisting with good generalization in overparameterized regimes.