Zeileis Conditional Variable Importance for Random Forests

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Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges (2020, methodology): “ample, [123,66,52]). Both regression models and 2 The random forest paper has been cited over 60,000 times (Google Scholar; September 2020) and there are many papers improving the importance measure ([110,111,44,56]) which are also cited frequently. IML - History, Methods, Challenges 3 Fig.1. Left: ”
Exploring the cloud of variable importance for the set of all good models (2020, background): “An alternative notion of variable importance (conditional variable importance) measures the importance of the ‘unique’ information of a variable that is not shared by other variables 3,20 .”
A comparative study of machine learning classifiers for modeling travel mode choice (2017, unknown): “Seemingly irrelevant variables may become important only in the221 context of others, while redundancies between variables may lead to an overestimation of importance (e.g.222 Strobl et al., 2008).”