Bias-Variance

Bias-Variance - Machine Learning Srihari Bias-Variance...

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Machine Learning Srihari 1 Bias-Variance Decomposition Choosing λ in maximum likelihood/least squares estimation Five part discussion: 1. On-line regression demo 2. Point estimate Chinese Emperor’s Height 3. Formulation for regression 4. Example 5. Choice of optimal λ
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Machine Learning Srihari Bias-Variance in Regression • Interactive demo at http://www.aiaccess.net/English/Glossaries/ GlosMod/e_gm_bias_variance.htm Low degree polynomial has high bias (fits poorly) but has low variance with different data sets High degree polynomial has low bias (fits well) but has high variance with different data sets 2
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Machine Learning Srihari Bias-Variance in Point Estimate Scenario 1 Everyone believes it is 180 (variance=0) Answer is always 180 The error is always -20 Ave squared error is 400 Average bias error is 20 400 =400+0 Scenario 2 Normally distributed beliefs with mean 180 and std dev 10 (variance 100) Poll two: One says 190, other 170 Bias Errors are -10 and -30
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This document was uploaded on 02/25/2012.

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Bias-Variance - Machine Learning Srihari Bias-Variance...

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