Computationally Efficient Feature Significance and Importance for Machine Learning Models

Enguerrand Horel; Kay Giesecke

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arXiv · 1905.09849

Computationally Efficient Feature Significance and Importance for Machine Learning Models

Enguerrand Horel·Kay Giesecke

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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.

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Enguerrand Horel, Kay Giesecke. 2019-05-23. Computationally Efficient Feature Significance and Importance for Machine Learning Models. https://arxiv.org/abs/1905.09849

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