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Big Data. 2015 Dec;3(4):267-76. doi: 10.1089/big.2015.0033.

A Bayesian Approach to Learning Scoring Systems.

Author information

1
1 Department of Computer Engineering, Orta Dogu Teknik Universitesi (ODTU) , Ankara, Turkey .
2
2 Sloan School of Management, Massachusetts Institute of Technology , Cambridge, Massachusetts.

Abstract

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.

KEYWORDS:

data mining; machine learning; predictive analytics

PMID:
27441407
DOI:
10.1089/big.2015.0033

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