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
Citation AnalysisPro
You’ll need to upgrade your plan to Pro
Looking to understand the true influence of a researcher’s work across journals & affiliations?
- Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
- Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.
Notes
History