Inferring feature importance with uncertainties with application to large genotype data.

ABSTRACT: 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 estimat

SUBMITTER: Johnsen PV 

PROVIDER: S-EPMC10038287 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature