xplainfi: Feature Importance Methods for Global Explanations
Abstract
Provides a consistent interface for common feature importance methods as described in Ewald et al. (2024) , including permutation feature importance (PFI), conditional and relative feature importance (CFI, RFI), leave one covariate out (LOCO), and Shapley additive global importance (SAGE), as well as feature sampling mechanisms to support conditional importance methods.
Fields of Study & Subjects
Explainable Artificial Intelligence (XAI)
Machine Learning and Data Classification
Software Testing and Debugging Techniques
Publication details
- Publication date
- 30 January 2026
- Document type
- Dataset