Nonparametric Variable Importance Assessment Using Machine Learning Techniques

• Activity Number: 233 - ASA Biometrics Section JSM Travel Awards (I)
• Type: Topic Contributed
• Date/Time: Monday, July 30, 2018 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Brian Williamson* and Peter Gilbert and Noah Simon and Marco Carone
• Companies: University of Washington and Fred Hutchinson Cancer Research Center and University of Washington and University of Washington
• Keywords: machine learning; nonparametric R-squared; statistical inference; targeted learning; variable importance

Abstract

In a regression setting, it is often of interest to quantify the importance of various features in predicting the response. Commonly, the variable importance measure used is determined by the regression technique employed. For this reason, practitioners often only resort to one of a few regression techniques for which a variable importance measure is naturally defined. Unfortunately, these regression techniques are often sub-optimal for predicting response. In this work, we study a novel variable importance measure that can be used with any regression technique, and whose interpretation is agnostic to the technique used. Specifically, we propose a generalization of the ANOVA variable importance measure, and discuss how it facilitates the use of machine learning techniques to flexibly estimate the variable importance of a single feature or group of features. We describe how to construct an efficient estimator of this measure as well as a valid confidence interval. Through simulations, we show that our proposal has good practical operating characteristics, and we illustrate its use with data from a study of risk factors for cardiovascular disease in South Africa.