Interpretable Random Forests via Rule Extraction

v1v2v3v4 (latest)

Interpretable Random Forests via Rule Extraction

29 April 2020

Clément Bénard

Sébastien Da Veiga

ArXiv (abs)PDF HTML

Papers citing "Interpretable Random Forests via Rule Extraction"

11 / 11 papers shown

Title
Explaining Predictions by Characteristic Rules Amr Alkhatib Henrik Bostrom Michalis Vazirgiannis 76 5 0 31 May 2024
Local and Regional Counterfactual Rules: Summarized and Robust Recourses Salim I. Amoukou Nicolas Brunel 47 0 0 29 Sep 2022
Explainable Global Fairness Verification of Tree-Based Classifiers Stefano Calzavara Lorenzo Cazzaro Claudio Lucchese Federico Marcuzzi 79 3 0 27 Sep 2022
Explaining Any ML Model? -- On Goals and Capabilities of XAI Moritz Renftle Holger Trittenbach M. Poznic Reinhard Heil ELM 63 6 0 28 Jun 2022
Consistent Sufficient Explanations and Minimal Local Rules for explaining regression and classification models Salim I. Amoukou Nicolas Brunel FAtt LRM 84 5 0 08 Nov 2021
Trading Complexity for Sparsity in Random Forest Explanations Gilles Audemard S. Bellart Louenas Bounia F. Koriche Jean-Marie Lagniez Pierre Marquis 61 40 0 11 Aug 2021
Accurate Shapley Values for explaining tree-based models Salim I. Amoukou Nicolas Brunel Tangi Salaun TDI FAtt 67 15 0 07 Jun 2021
Making CNNs Interpretable by Building Dynamic Sequential Decision Forests with Top-down Hierarchy Learning Yilin Wang Shaozuo Yu Xiaokang Yang Wei Shen 38 1 0 05 Jun 2021
Robust Model Compression Using Deep Hypotheses Omri Armstrong Ran Gilad-Bachrach OOD 26 2 0 13 Mar 2021
Towards interpreting ML-based automated malware detection models: a survey Yuzhou Lin Xiaolin Chang 124 7 0 15 Jan 2021
Consistent Regression using Data-Dependent Coverings Vincent Margot Jean-Patrick Baudry Frédéric Guilloux Olivier Wintenberger 52 5 0 04 Jul 2019