A Provable Algorithm for Learning Interpretable Scoring Systems
Sokolovska, Nataliya; Chevaleyre, Yann; Zucker, Jean-Daniel (2018), A Provable Algorithm for Learning Interpretable Scoring Systems, in Amos Storkey; Fernando Perez-Cruz, Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research (PMLR), p. 566-574
TypeCommunication / Conférence
External document linkhttps://proceedings.mlr.press/v84/sokolovska18a.html
Conference title21st International Conference on Artificial Intelligence and Statistics (AISTATS)
Book titleProceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics
Book authorAmos Storkey; Fernando Perez-Cruz
Number of pages2074
MetadataShow full item record
Nutrition et obésités: approches systémiques (UMR-S 1269) [Nutriomics]
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Unité de modélisation mathématique et informatique des systèmes complexes [Bondy] [UMMISCO]
Abstract (EN)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.
Showing items related by title and author.
Thanh Hai, Nguyen; Chevaleyre, Yann; Prifti, Edi; Sokolovska, Nataliya; Zucker, Jean-Daniel (2017) Communication / Conférence
Nguyen, Thanh Hai; Prifti, Edi; Chevaleyre, Yann; Sokolovska, Nataliya; Zucker, Jean-Daniel (2018) Communication / Conférence