Computationally Efficient Feature Significance and Importance for Machine Learning Models
Machine Learning
2019-10-15 v2 Machine Learning
Abstract
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.
Cite
@article{arxiv.1905.09849,
title = {Computationally Efficient Feature Significance and Importance for Machine Learning Models},
author = {Enguerrand Horel and Kay Giesecke},
journal= {arXiv preprint arXiv:1905.09849},
year = {2019}
}