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A Bayesian Approach to Learning Scoring Systems
Date
2015-12-01
Author
Ertekin Bolelli, Şeyda
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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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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.