10-601 Bias-Variance
From Cohen Courses
Slides
- William's Slides in Powerpoint
Readings
- Bishop: Chap 1, 2
- Mitchell: Chap 5, 6
- 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.
