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Decision trees (DTs) embody interpretable classifiers.
The magical number seven, plus or minus two: Some limits on our capacity for processing information
George A Miller · 1956
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Decision tree induction based on efficient tree restructuring
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Statistical modeling: The two cultures
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Optimal sparse decision trees
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
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Learning optimal decision trees using constraint programming
Hélène Verhaeghe, Siegfried Nijssen, Gilles Pesant, Claude-Guy Quimper, and Pierre Schaus · 2020
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On the computational intelligibility of boolean classifiers
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Provably efficient, succinct, and precise explanations
Guy Blanc, Jane Lange, and Li-Yang Tan · 2021
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ASTERYX: A model-agnostic sat-based approach for symbolic and score-based explanations
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Artificial Intelligence Act
EU · 2021
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https://archive.ics.uci.edu/ml , 2020
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On explaining random forests with SAT
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On guaranteed optimal robust explanations for NLP models
Emanuele La Malfa, Rhiannon Michelmore, Agnieszka M. Zbrzezny, Nicola Paoletti, and Marta Kwiatkowska · 2021
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SAT-based decision tree learning for large data sets
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The computational complexity of understanding binary classifier decisions
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Probabilistic Sufficient Explanations
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