research paper

Computationally Efficient Feature Significance and Importance for Machine Learning Models

Kay Giesecke

Founder, Chairman and Chief Scientist at Infima, Professor at Stanford University

Enguerrand Horel

Senior Research Scientist, Upstart

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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