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One of the key elements of explanatory analysis of a predictive model is to assess the importance of individual variables.
Uci repository of machine learning databases, 1998
D. Newman, S. Hettich, C. Blake, and C. Merz · 1998
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Explaining Classifications For Individual Instances
M. Robnik-Sikonja and I. Kononenko · 2007
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An Introduction to Statistical Learning
G. James, D. Witten, T. Hastie, and R. Tibshirani · 2013
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Grouped variable importance with random forests and application to multiple functional data analysis
B. Gregorutti, B. Michel, and P. Saint-Pierre · 2015
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"Why Should I Trust You?": Explaining the Predictions of Any Classifier
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
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R package "corrplot": Visualization of a Correlation Matrix , 2017
T. Wei and V. Simko · 2017
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DALEX: Explainers for Complex Predictive Models in R
P. Biecek · 2018
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iml: An R package for Interpretable Machine Learning
C. Molnar, B. Bischl, and G. Casalicchio · 2018
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All models are wrong, but many are useful: Learning a variable’s importance by studying an entire class of prediction models simultaneously
A. Fisher, C. Rudin, and F. Dominici · 2019
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cluster: Cluster Analysis Basics and Extensions , 2019
M. Maechler, P. Rousseeuw, A. Struyf, M. Hubert, and K. Hornik · 2019
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lime: Local Interpretable Model-Agnostic Explanations , 2019
T. L. Pedersen and M. Benesty · 2019
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localModel: LIME-Based Explanations with Interpretable Inputs Based on Ceteris Paribus Profiles , 2019
M. Staniak, P. Biecek, K. Igras, and A. Gosiewska · 2019
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vip: Variable Importance Plots , 2020
B. Greenwell, B. Boehmke, and B. Gray · 2020
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corrr: Correlations in R , 2020
M. Kuhn, S. Jackson, and J. Cimentada · 2020
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corrgrapher: Explore Correlations Between Variables in a Machine Learning Model , 2020
P. Morgen and P. Biecek · 2020
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vimp: Perform Inference on Algorithm-Agnostic Variable Importance , 2020
B. D. Williamson, N. Simon, and M. Carone · 2020
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