A bayesian approach to learning scoring systems

Published

Journal Article

© Mary Ann Liebert, Inc. 2015. 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