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Tree ensembles, such as random forests and AdaBoost, are ubiquitous machine learning models known for achieving strong predictive performance across a wide variety of domains.
Classification and regression trees
Leo Breiman, Jerome Friedman, RA Olshen, and Charles J Stone · 1984
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Knowledge acquisition and explanation for multi-attribute decision making
Marko Bohanec and Vladislav Rajkovic · 1988
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Experiments with a new boosting algorithm
Yoav Freund, Robert E Schapire, et al · 1996
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Random forests
Leo Breiman · 2001
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Greedy function approximation: a gradient boosting machine
Jerome H Friedman · 2001
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Conditional variable importance for random forests
Carolin Strobl, Anne-Laure Boulesteix, Thomas Kneib, Thomas Augustin, and Achim Zeileis · 2008
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Predictive learning via rule ensembles
Jerome H Friedman, Bogdan E Popescu, et al · 2008
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Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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The emergence of machine learning techniques in criminology
Tim Brennan and William L Oliver · 2013
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Event labeling combining ensemble detectors and background knowledge
Hadi Fanaee-T and Joao Gama · 2014
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Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation
Alex Goldstein, Adam Kapelner, Justin Bleich, and Emil Pitkin · 2015
Cited alongside, same era.
European union regulations on algorithmic decision-making and a" right to explanation"
Bryce Goodman and Seth Flaxman · 2016
Cited alongside, same era.
Why should i trust you?: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Cited alongside, same era.
Visualizing the effects of predictor variables in black box supervised learning models
Daniel W Apley · 2016
Cited alongside, same era.
Pmlb: a large benchmark suite for machine learning evaluation and comparison
Randal S Olson, William La Cava, Patryk Orzechowski, Ryan J Urbanowicz, and Jason H Moore · 2017
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Consistent individualized feature attribution for tree ensembles
Scott M Lundberg, Gabriel G Erion, and Su-In Lee · 2018
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iterative random forests to discover predictive and stable high-order interactions
Sumanta Basu, Karl Kumbier, James B Brown, and Bin Yu · 2018
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Refining interaction search through signed iterative random forests
Karl Kumbier, Sumanta Basu, James B Brown, Susan Celniker, and Bin Yu · 2018
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Hierarchical interpretations for neural network predictions
Chandan Singh, W James Murdoch, and Bin Yu · 2018
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Diagnosing a disorder in a classification benchmark
James McDermott and Richard S Forsyth · 2016
Cited alongside, same era.
A survey on deep learning in medical image analysis
Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen AWM van der Laak, Bram van Ginneken, and Clara I Sánchez · 2017
Cited alongside, same era.
Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Detecting statistical interactions from neural network weights
Michael Tsang, Dehua Cheng, and Yan Liu · 2017
Cited alongside, same era.
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Beyond word importance: Contextual decomposition to extract interactions from lstms
W James Murdoch, Peter J Liu, and Bin Yu · 2018
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Interpretable machine learning
Christoph Molnar · 2018
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Interpretable machine learning: definitions, methods, and applications
W James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, and Bin Yu · 2019
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Quantifying interpretability of arbitrary machine learning models through functional decomposition
Christoph Molnar, Giuseppe Casalicchio, and Bernd Bischl · 2019
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