Bias-Variance Tradeoff

Every model error comes from one of three sources: bias, variance, or noise. You can only control the first two.

Learn 1.3 hr Python

Prerequisites: Phase 2, Lessons 01-09 (ML basics, regression, classification, evaluation)

#Learning Objectives

Derive the bias-variance decomposition of expected prediction error and explain the role of irreducible noise
Diagnose whether a model suffers from high bias or high variance using training and test error patterns
Explain how regularization techniques (L1, L2, dropout, early stopping) trade bias for variance
Implement experiments that visualize the bias-variance tradeoff across models of increasing complexity

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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.