A Provable Algorithm for Learning Interpretable Scoring Systems

Nataliya Sokolovska, Yann Chevaleyre, Jean-Daniel Zucker
Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, PMLR 84:566-574, 2018.

Abstract

Score learning aims at taking advantage of supervised learning to produce interpretable models which facilitate decision making. Scoring systems are simple classification models that let users quickly perform stratification. Ideally, a scoring system is based on simple arithmetic operations, is sparse, and can be easily explained by human experts. In this contribution, we introduce an original methodology to simultaneously learn interpretable binning mapped to a class variable, and the weights associated with these bins contributing to the score. We develop and show the theoretical guarantees for the proposed method. We demonstrate by numerical experiments on benchmark data sets that our approach is competitive compared to the state-of-the-art methods. We illustrate by a real medical problem of type 2 diabetes remission prediction that a scoring system learned automatically purely from data is comparable to one manually constructed by clinicians.

Cite this Paper

BibTeX
@InProceedings{pmlr-v84-sokolovska18a, title = {A Provable Algorithm for Learning Interpretable Scoring Systems}, author = {Sokolovska, Nataliya and Chevaleyre, Yann and Zucker, Jean-Daniel}, booktitle = {Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics}, pages = {566--574}, year = {2018}, editor = {Storkey, Amos and Perez-Cruz, Fernando}, volume = {84}, series = {Proceedings of Machine Learning Research}, month = {09--11 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v84/sokolovska18a/sokolovska18a.pdf}, url = {https://proceedings.mlr.press/v84/sokolovska18a.html}, abstract = {Score learning aims at taking advantage of supervised learning to produce interpretable models which facilitate decision making. Scoring systems are simple classification models that let users quickly perform stratification. Ideally, a scoring system is based on simple arithmetic operations, is sparse, and can be easily explained by human experts. In this contribution, we introduce an original methodology to simultaneously learn interpretable binning mapped to a class variable, and the weights associated with these bins contributing to the score. We develop and show the theoretical guarantees for the proposed method. We demonstrate by numerical experiments on benchmark data sets that our approach is competitive compared to the state-of-the-art methods. We illustrate by a real medical problem of type 2 diabetes remission prediction that a scoring system learned automatically purely from data is comparable to one manually constructed by clinicians. } }
Endnote
%0 Conference Paper %T A Provable Algorithm for Learning Interpretable Scoring Systems %A Nataliya Sokolovska %A Yann Chevaleyre %A Jean-Daniel Zucker %B Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2018 %E Amos Storkey %E Fernando Perez-Cruz %F pmlr-v84-sokolovska18a %I PMLR %P 566--574 %U https://proceedings.mlr.press/v84/sokolovska18a.html %V 84 %X Score learning aims at taking advantage of supervised learning to produce interpretable models which facilitate decision making. Scoring systems are simple classification models that let users quickly perform stratification. Ideally, a scoring system is based on simple arithmetic operations, is sparse, and can be easily explained by human experts. In this contribution, we introduce an original methodology to simultaneously learn interpretable binning mapped to a class variable, and the weights associated with these bins contributing to the score. We develop and show the theoretical guarantees for the proposed method. We demonstrate by numerical experiments on benchmark data sets that our approach is competitive compared to the state-of-the-art methods. We illustrate by a real medical problem of type 2 diabetes remission prediction that a scoring system learned automatically purely from data is comparable to one manually constructed by clinicians.
APA
Sokolovska, N., Chevaleyre, Y. & Zucker, J.. (2018). A Provable Algorithm for Learning Interpretable Scoring Systems. Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 84:566-574 Available from https://proceedings.mlr.press/v84/sokolovska18a.html.

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