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Generalized additive models (GAMs) have become a leading modelclass for interpretable machine learning.
A comparison of GCV and GML for choosing the smoothing parameter in the generalized spline smoothing problem
Grace Wahba. 1985 · 1985
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Generalized Additive Models
Trevor Hastie and Rob Tibshirani. 1990 · 1990
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Spline models for observational data
Grace Wahba. 1990 · 1990
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Generalized additive models for medical research
Trevor Hastie and Robert Tibshirani. 1995 · 1995
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Regression shrinkage and selection via the lasso
Robert Tibshirani. 1996 · 1996
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An empirical comparison of voting classification algorithms: Bagging, boosting, and variants
Eric Bauer and Ron Kohavi. 1999 · 1999
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Sparsity and smoothness via the fused lasso
Robert Tibshirani, Michael Saunders, Saharon Rosset, Ji Zhu, and Keith Knight. 2005 · 2005
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Decision Tree Instability and Active Learning. In
Kenneth Dwyer and Robert Holte. 2007 · 2007
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Bias in random forest variable importance measures: Illustrations, sources and a solution
Carolin Strobl, Anne-Laure Boulesteix, Achim Zeileis, and Torsten Hothorn. 2007 · 2007
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A comparison of methods for the fitting of generalized additive models
Harald Binder and Gerhard Tutz. 2008 · 2008
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The elements of statistical learning: data mining, inference, and prediction
Trevor Hastie, Robert Tibshirani, and Jerome Friedman. 2009 · 2009
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On feature selection, bias-variance, and bagging. In
M Arthur Munson and Rich Caruana. 2009 · 2009
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To explain or to predict?
Galit Shmueli. 2010 · 2010
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Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models
S. N. Wood. 2011 · 2011
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Intelligible models for classification and regression. In
Yin Lou, Rich Caruana, and Johannes Gehrke. 2012 · 2012
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Generalized additive models in business and economics
K Sapra. 2013 · 2013
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Learning fair representations. In
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork. 2013 · 2013
Cited alongside, same era.
Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In
Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad. 2015 · 2015
Cited alongside, same era.
XGBoost: A Scalable Tree Boosting System. In
Tianqi Chen and Carlos Guestrin. 2016 · 2016
Cited alongside, same era.
Have we substantially underestimated the impact of improved sanitation coverage on child health? A generalized additive model panel analysis of global data on child mortality and malnutrition
Paul R Hunter and Annette Prüss-Ustün. 2016 · 2016
Cited alongside, same era.
MIMIC-III, a freely accessible critical care database
Learning global additive explanations for neural nets using model distillation
Sarah Tan, Rich Caruana, Giles Hooker, Paul Koch, and Albert Gordo. 2018b · 2018
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Machine Bias: There’s software used across the country to predict future criminals. And it’s biased against blacks
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2019 · 2019
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Please Stop Permuting Features: An Explanation and Alternatives
Giles Hooker and Lucas Mentch. 2019 · 2019
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A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. 2019 · 2019
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InterpretML: A Unified Framework for Machine Learning Interpretability
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Alistair EW Johnson, Tom J Pollard, Lu Shen, H Lehman Li-Wei, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. 2016 · 2016
Cited alongside, same era.
Fused lasso additive model
Ashley Petersen, Daniela Witten, and Noah Simon. 2016 · 2016
Cited alongside, same era.
“Why Should I Trust You?": Explaining the Predictions of Any Classifier. In
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Cited alongside, same era.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova. 2017 · 2017
Cited alongside, same era.
Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez and Been Kim. 2017 · 2017
Cited alongside, same era.
UCI Machine Learning Repository
Dheeru Dua and Casey Graff. 2017 · 2017
Cited alongside, same era.
A Unified Approach to Interpreting Model Predictions. In
Scott M Lundberg and Su-In Lee. 2017 · 2017
Cited alongside, same era.
Harsha Nori, Samuel Jenkins, Paul Koch, and Rich Caruana. 2019 · 2019
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Hierarchical generalized additive models in ecology: an introduction with mgcv
Eric J Pedersen, David L Miller, Gavin L Simpson, and Noam Ross. 2019 · 2019
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Generalized additive models: Building evidence of air pollution, climate change and human health
Khaiwal Ravindra, Preety Rattan, Suman Mor, and Ashutosh Nath Aggarwal. 2019 · 2019
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Additive models with trend filtering
Veeranjaneyulu Sadhanala and Ryan J Tibshirani. 2019 · 2019
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Splitting on categorical predictors in random forests
Marvin N Wright and Inke R König. 2019 · 2019
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An Evaluation of the Doctor-Interpretability of Generalized Additive Models with Interactions. In
Stefan Hegselmann, Thomas Volkert, Hendrik Ohlenburg, Antje Gottschalk, Martin Dugas, and Christian Ertmer. 2020 · 2020
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Generalized additive models to capture the death rates in Canada COVID-19
Farzali Izadi. 2020 · 2020
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The optimal cut-off point of vitamin D for pregnancy outcomes using a generalized additive model
Maryam Rostami, Masoumeh Simbar, Mina Amiri, Razieh Bidhendi-Yarandi, Farhad Hosseinpanah, and Fahimeh Ramezani Tehrani. 2020 · 2020
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Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods. In
Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, and Himabindu Lakkaraju. 2020 · 2020
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Unbiased Measurement of Feature Importance in Tree-Based Methods
Zhengze Zhou and Giles Hooker. 2021 · 2021
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