Lecture 15 – Bias and Variance
Presented by Joey Gonzalez, Andrew Bray, Fernando Perez, and Ani Adhikari
- 15.5 slides (1, 2)
- video playlist
- Introduction to Overfitting
- Bias-Variance decomposition derivation (raw)
Important: The algebra behind the decomposition of model risk into observational variance, model variance, and bias, is not in the slides or video but is in the link above. You should read it after watching this lecture. Also, you may want to review Lecture 3 for a refresher on random variables.
A quick Introduction to Overfitting and Generalization.
Variance of random variables. Walking through an alternate calculation of variance. Variance of a linear transformation.
Introducing the data generating process and prediction error. Model risk.
Components of the Prediction Error.
Visualizing Bias and Variance.
Model Complexity and the Bias Variance Tradeoff.