Difference between revisions of "10-601 Bias-Variance"
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=== Slides === | === Slides === | ||
| − | * William's [http://www.cs.cmu.edu/~wcohen/10-601/bias-variance.ppt Slides in Powerpoint] | + | * William's [http://www.cs.cmu.edu/~wcohen/10-601/bias-variance.ppt Slides in Powerpoint], and [http://www.cs.cmu.edu/~wcohen/10-601/bias-variance.pdf in PDF] |
=== Readings === | === Readings === | ||
Latest revision as of 10:44, 20 October 2014
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.
