# Relative Feature Importance

arXiv: https://arxiv.org/abs/2007.08283 · HTML: https://mlsanity.com/paper/2007.08283/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2020.

## Built on by (most cited first, 12 of 12)

- [Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena](https://mlsanity.com/paper/2206.05487/) (2022)
- [Comparing Explanation Methods for Traditional Machine Learning Models Part 1: An Overview of Current Methods and Quantifying Their Disagreement](https://mlsanity.com/paper/2211.08943/) (2022)
- [A Guide to Feature Importance Methods for Scientific Inference](https://mlsanity.com/paper/2404.12862/) (2024)
- [Incremental Permutation Feature Importance (iPFI): Towards Online Explanations on Data Streams](https://mlsanity.com/paper/2209.01939/) (2022)
- [Disentangling Interactions and Dependencies in Feature Attribution](https://mlsanity.com/paper/2410.23772/) (2024)
- [iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams](https://mlsanity.com/paper/2303.01181/) (2023)
- [Large Scale Diverse Combinatorial Optimization: ESPN Fantasy Football Player Trades](https://mlsanity.com/paper/2111.02859/) (2021)
- [Learning to Increase the Power of Conditional Randomization Tests](https://mlsanity.com/paper/2207.01022/) (2022)
- [Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT)](https://mlsanity.com/paper/2106.08086/) (2021)
- [Interpretable Architecture Neural Networks for Function Visualization](https://mlsanity.com/paper/2303.03393/) (2023)
- [xplainfi: Feature Importance and Statistical Inference for Machine Learning in R](https://mlsanity.com/paper/2603.15306/) (2026)
- [Towards Daily High-resolution Inundation Observations using Deep Learning and EO](https://mlsanity.com/paper/2208.09135/) (2022)

## Abstract (arXiv)

Interpretable Machine Learning (IML) methods are used to gain insight into the relevance of a feature of interest for the performance of a model. Commonly used IML methods differ in whether they consider features of interest in isolation, e.g., Permutation Feature Importance (PFI), or in relation to all remaining feature variables, e.g., Conditional Feature Importance (CFI). As such, the perturbation mechanisms inherent to PFI and CFI represent extreme reference points. We introduce Relative Feature Importance (RFI), a generalization of PFI and CFI that allows for a more nuanced feature importance computation beyond the PFI versus CFI dichotomy. With RFI, the importance of a feature relative to any other subset of features can be assessed, including variables that were not available at training time. We derive general interpretation rules for RFI based on a detailed theoretical analysis of the implications of relative feature relevance, and demonstrate the method's usefulness on simulated examples.