Bias plus variance decomposition for zero-one loss functions (1996)
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| Venue: | In Machine Learning: Proceedings of the Thirteenth International Conference |
| Citations: | 144 - 3 self |
BibTeX
@INPROCEEDINGS{Kohavi96biasplus,
author = {Ron Kohavi},
title = {Bias plus variance decomposition for zero-one loss functions},
booktitle = {In Machine Learning: Proceedings of the Thirteenth International Conference},
year = {1996},
pages = {275--283}
}
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Abstract
We present a bias-variance decomposition of expected misclassi cation rate, the most commonly used loss function in supervised classi cation learning. The bias-variance decomposition for quadratic loss functions is well known and serves as an important tool for analyzing learning algorithms, yet no decomposition was o ered for the more commonly used zero-one (misclassi cation) loss functions until the recent work of Kong & Dietterich (1995) and Breiman (1996). Their decomposition su ers from some major shortcomings though (e.g., potentially negative variance), which our decomposition avoids. We show that, in practice, the naive frequency-based estimation of the decomposition terms is by itself biased and show how to correct for this bias. We illustrate the decomposition on various algorithms and datasets from the UCI repository. 1
