  Estimating feature importance is a significant 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 feature importance measure of SAGE (Shapley
additive global importance) and introduce sub-SAGE which can be estimated
without resampling for tree-based models. We argue that the uncertainties can
be estimated from bootstrapping and demonstrate the approach for tree ensemble
methods. The framework is exemplified on synthetic data as well as
high-dimensional genomics data.
