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Boosted trees is a dominant ML model, exhibiting high accuracy.
Random forests
L. Breiman · 2001
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Greedy function approximation
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XGBoost: A scalable tree boosting system
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M. T. Ribeiro, S. Singh, and C. Guestrin · 2018
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Formal verification of Bayesian network classifiers
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A symbolic approach to explaining bayesian network classifiers
A. Shih, A. Choi, and A. Darwiche · 2018
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R. Guidotti, A. Monreale, S. Ruggieri, F. Turini, F. Giannotti, and D. Pedreschi · 2019
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A benchmark for interpretability methods in deep neural networks
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From local explanations to global understanding with explainable AI for trees
S. M. Lundberg, G. G. Erion, H. Chen, A. J. DeGrave, J. M. Prutkin, B. Nair, R. Katz, J. Himmelfarb, N. Bansal, and S.I. Lee · 2020
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Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI
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Model interpretability through the lens of computational complexity
P. Barceló, M. Monet, J. Pérez, and B. Subercaseaux · 2020
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V. Borisov, T. Leemann, K. Seßler, J. Haug, M. Pawelczyk, and G. Kasneci · 2021
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On efficiently explaining graph-based classifiers
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On explaining random forests with SAT
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Optimal counterfactual explanations in tree ensembles
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Interpretable machine learning: Fundamental principles and 10 grand challenges
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Trading complexity for sparsity in random forest explanations
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Using MaxSAT for efficient explanations of tree ensembles
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