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Interpretability is Harder in the Multiclass Setting: Axiomatic Interpretability for Multiclass Additive Models.

, , , , , and . CoRR, (2018)

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Feature Augmentation with Reinforcement Learning., , , , , and . ICDE, page 3360-3372. IEEE, (2022)Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data., , , , , and . PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, page 534-551. Springer, (2021)Accurate intelligible models with pairwise interactions., , , and . KDD, page 623-631. ACM, (2013)Intelligible models for classification and regression., , and . KDD, page 150-158. ACM, (2012)MatchMiner: Efficient Spanning Structure Mining in Large Image Collections., , and . ECCV (2), volume 7573 of Lecture Notes in Computer Science, page 45-58. Springer, (2012)On Finding Bi-objective Pareto-optimal Fraud Prevention Rule Sets for Fintech Applications., and . KDD, page 5959-5968. ACM, (2024)Efficiently Training Intelligible Models for Global Explanations., , , and . CIKM, page 2637-2644. ACM, (2020)BDT: Gradient Boosted Decision Tables for High Accuracy and Scoring Efficiency., and . KDD, page 1893-1901. ACM, (2017)Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation., , , and . AIES, page 303-310. ACM, (2018)Screening and Identification of Key Biomarkers in Melanoma: Evidence from Bioinformatic Analyses., , , , and . J. Comput. Biol., 28 (3): 317-329 (2021)