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In the past years, many new explanation methods have been proposed to achieve interpretability of machine learning predictions.
An Evaluation of the Human-Interpretability of Explanation
Isaac Lage, Emily Chen, Jeffrey He, Menaka Narayanan, Been Kim, Sam Gershman, and Finale Doshi-Velez. 2019 · 1902
Earlier work this paper cites.
Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach
Fred G. Paas. 1992 · 1992
Earlier work this paper cites.
On the sample size for one-sided equivalence of sensitivities based upon McNemar's test
Ying Lu and Judy A. Bean. 1995 · 1995
Earlier work this paper cites.
An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models
Johan Huysmans, Karel Dejaeger, Christophe Mues, Jan Vanthienen, and Bart Baesens. 2011 · 2010
Earlier work this paper cites.
User-oriented Assessment of Classification Model Understandability
Allahyari Hiva and Lavesson Niklas. 2011 · 2011
Earlier work this paper cites.
Scikit-learn: Machine Learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011 · 2011
Earlier work this paper cites.
OpenML: Networked Science in Machine Learning
Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, and Luis Torgo. 2013 · 2013
Earlier work this paper cites.
Explaining prediction models and individual predictions with feature contributions
Erik Štrumbelj and Igor Kononenko. 2014 · 2014
Cited alongside, same era.
Calibrated Structured Prediction
Volodymyr Kuleshov and Percy S Liang. 2015 · 2015
Cited alongside, same era.
Interpretable Decision Sets: A Joint Framework for Description and Prediction. In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’16) . ACM, New York, NY, USA, 1675–1684
Himabindu Lakkaraju, Stephen H Bach, and Jure Leskovec. 2016 · 2016
Cited alongside, same era.
The Mythos of Model Interpretability
Zachary C. Lipton. 2016 · 2016
Cited alongside, same era.
"Why Should I Trust You?": Explaining the Predictions of Any Classifier. In Proceedings of the 22nd ACM SIG International Conference on Knowledge Discovery and Data Mining (KDD) . ACM Press, New York, New York, USA, 1135–1144
UCI Machine Learning Repository
Dheeru Dua and Karra Taniskidou Efi. 2017 · 2017
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Have It Both Ways - From A/B Testing to A&B Testing with Exceptional Model Mining. In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017 . 114–126
Wouter Duivesteijn, Tara Farzami, Thijs Putman, Evertjan Peer, Hilde J. P. Weerts, Jasper N. Adegeest, Gerson Foks, and Mykola Pechenizkiy. 2017 · 2017
Later among the works it cites.
A Unified Approach to Interpreting Model Predictions
Scott M Lundberg and Su-In Lee. 2017 · 2017
Later among the works it cites.
Educational Data Mining and Analysis of Students’ Academic Performance Using WEKA
Sadiq Hussain, Neama Abdulaziz Dahan, Fadl Mutaher Ba-Alwi, and Najoua Ribata. 2018 · 2018
Later among the works it cites.
Consistent Individualized Feature Attribution for Tree Ensembles
Scott M. Lundberg, Gabriel G. Erion, and Su-In Lee. 2018 · 2018
Later among the works it cites.
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Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Cited alongside, same era.
Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez and Been Kim. 2017 · 2017
Cited alongside, same era.
Manipulating and Measuring Model Interpretability
Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake M. Hofman, Jennifer Wortman Vaughan, and Hanna Wallach. 2018 · 2018
Later among the works it cites.