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Les raisons majoritaires : des explications abductives pour les forêts aléatoires.

, , , , , and . EGC, volume E-38 of RNTI, page 123-134. Editions RNTI, (2022)

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Trading Complexity for Sparsity in Random Forest Explanations., , , , , and . AAAI, page 5461-5469. AAAI Press, (2022)Sur les explications abductives préférées pour les arbres de décision et les forêts aléatoires., , , , , and . EGC, volume E-39 of RNTI, page 507-514. Editions RNTI, (2023)Sur le pouvoir explicatif des arbres de décision., , , , , and . EGC, volume E-38 of RNTI, page 147-158. Editions RNTI, (2022)Computing Abductive Explanations for Boosted Regression Trees., , , and . IJCAI, page 3432-3441. ijcai.org, (2023)PyXAI : calculer en Python des explications pour des modèles d'apprentissage supervisé., , , , , and . EGC, volume E-39 of RNTI, page 581-588. Editions RNTI, (2023)Les raisons majoritaires : des explications abductives pour les forêts aléatoires., , , , , and . EGC, volume E-38 of RNTI, page 123-134. Editions RNTI, (2022)Trading Complexity for Sparsity in Random Forest Explanations., , , , , and . CoRR, (2021)On the Explanatory Power of Decision Trees., , , , , and . CoRR, (2021)On Preferred Abductive Explanations for Decision Trees and Random Forests., , , , , and . IJCAI, page 643-650. ijcai.org, (2022)On the Computational Intelligibility of Boolean Classifiers., , , , , and . KR, page 74-86. (2021)