# 10-601 Bias-Variance

From Cohen Courses

Jump to navigationJump to search### Slides

- William's Slides in Powerpoint, and in PDF

### Readings

- This isn't covered well in Mitchell. Valentini and Dietterich is a good source for bias-variance for classification. Wikipedia has a reasonable description of the regression case, which goes back at least to Geman et al 1992.
- See also Littman/Isbell on overfitting

### What you should know

- How overfitting/underfitting can be understood as a tradeoff between high-bias and high-variance learners.
- Mathematically, how to decompose error for linear regression into bias and variance.
- Intuitively, how classification can be decomposed into bias and variance.
- Which sorts of classifier variants lead to more bias and/or more variance: e.g., large vs small k in k-NN, etc.