A General Framework for Inference on Algorithm-Agnostic Variable Importance

Volume: 118, Issue: 543, Pages: 1645 - 1658
Published: Jan 5, 2022
Abstract
In many applications, it is of interest to assess the relative contribution of features (or subsets of features) toward the goal of predicting a response—in other words, to gauge the variable importance of features. Most recent work on variable importance assessment has focused on describing the importance of features within the confines of a given prediction algorithm. However, such assessment does not necessarily characterize the prediction...
Paper Details
Title
A General Framework for Inference on Algorithm-Agnostic Variable Importance
Published Date
Jan 5, 2022
Volume
118
Issue
543
Pages
1645 - 1658
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