Bias–Variance and How to Actually Diagnose Overfitting

Mon, 23 Mar 2026 00:00:00 +0000

Everyone can recite “high bias is underfitting, high variance is overfitting.” Far fewer can look at a training run and say which one they have and what to do about it. This post does both: first the bias–variance decomposition tightly enough to be useful, then a practical playbook — learning curves, the train/validation gap, cross-validation, and the traps — for diagnosing overfitting on a real model. It’s the diagnostic companion to the L1/L2 and dropout posts, which cover the fixes.

Cross-Validation on Juntak Noh — AI Notes

Bias–Variance and How to Actually Diagnose Overfitting