A bayesian approach to learning scoring systems
Journal Article (Journal Article)
We present a Bayesian method for building scoring systems, which are linear models with coefficients that have very few significant digits. Usually the construction of scoring systems involve manual effort - humans invent the full scoring system without using data, or they choose how logistic regression coefficients should be scaled and rounded to produce a scoring system. These kinds of heuristics lead to suboptimal solutions. Our approach is different in that humans need only specify the prior over what the coefficients should look like, and the scoring system is learned from data. For this approach, we provide a Metropolis-Hastings sampler that tends to pull the coefficient values toward their "natural scale." Empirically, the proposed method achieves a high degree of interpretability of the models while maintaining competitive generalization performances.
Full Text
Duke Authors
Cited Authors
- Ertekin, Ş; Rudin, C
Published Date
- December 1, 2015
Published In
Volume / Issue
- 3 / 4
Start / End Page
- 267 - 276
Electronic International Standard Serial Number (EISSN)
- 2167-647X
International Standard Serial Number (ISSN)
- 2167-6461
Digital Object Identifier (DOI)
- 10.1089/big.2015.0033
Citation Source
- Scopus