Interpretable Random Forests via Rule Extraction

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Interpretable Random Forests via Rule Extraction

29 April 2020

Clément Bénard

Sébastien Da Veiga

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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 64 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