Phase 2: Ensembles & Evaluation · ~75 minutes · Python
Bias-Variance Tradeoff
Every model error comes from one of three sources: bias, variance, or noise. You can only control the first two.
What you will learn
- 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
Introduction
- Type: Learn
- Language: Python
- Prerequisites: ML basics, Linear Regression, classification, Model Evaluation
- Time: ~75 minutes
Objective
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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