Journal Article

Inferring feature importance with uncertainties with application to large genotype data

Strümke, Inga, Langaas, Mette, Riemer-Sørensen, Signe, DeWan, Andrew Thomas, Johnsen, Pål Vegard

2023

Overview

Estimating feature importance, which is the contribution of a prediction or several predictions due to a feature, is an essential aspect of explaining data-based models. Besides explaining the model itself, an equally relevant question is which features are important in the underlying data generating process. We present a Shapley-value-based framework for inferring the importance of individual features, including uncertainty in the estimator. We build upon the recently published model-agnostic feature importance score of SAGE (Shapley additive global importance) and introduce Sub-SAGE. For tree-based models, it has the advantage that it can be estimated without computationally expensive resampling. We argue that for all model types the uncertainties in our Sub-SAGE estimator can be estimated using bootstrapping and demonstrate the approach for tree ensemble methods. The framework is exemplified on synthetic data as well as large genotype data for predicting feature importance with respect to obesity.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Artificial intelligence / Biobanks / Biology and Life Sciences / Computer and Information Sciences / Confidence intervals / Decomposition / Engineering and Technology / Estimation theory / Expected values / Game theory / Genotype / Genotype & phenotype / Genotypes / Genotyping Techniques / Machine learning / Neural networks / Obesity / Physical Sciences / Random variables / Resampling / Research and Analysis Methods / Synthetic data / Tree structures (Computers) / Uncertainty / Values