A Bayesian Approach to Learning Scoring Systems

Date

2015-12-01

Author

Ertekin Bolelli, Şeyda

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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 efforthumans 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.

Subject Keywords

Predictive analytics, Machine learning, Data mining

URI

https://hdl.handle.net/11511/42890

Journal

BIG DATA

DOI

https://doi.org/10.1089/big.2015.0033

Collections

Department of Computer Engineering, Article

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Citation Formats

Ş. Ertekin Bolelli, “A Bayesian Approach to Learning Scoring Systems,” BIG DATA, pp. 267–276, 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/42890.