# Computationally Efficient Feature Significance and Importance for Machine Learning Models

arXiv: https://arxiv.org/abs/1905.09849 · HTML: https://mlsanity.com/paper/1905.09849/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2019.

## Built on by (most cited first, 2 of 2)

- [On the price of explainability for some clustering problems](https://mlsanity.com/paper/2101.01576/) (2021)
- [KnAC: an approach for enhancing cluster analysis with background knowledge and explanations](https://mlsanity.com/paper/2112.08759/) (2021)

## Abstract (arXiv)

We develop a simple and computationally efficient significance test for the features of a machine learning model. Our forward-selection approach applies to any model specification, learning task and variable type. The test is non-asymptotic, straightforward to implement, and does not require model refitting. It identifies the statistically significant features as well as feature interactions of any order in a hierarchical manner, and generates a model-free notion of feature importance. Experimental and empirical results illustrate its performance.