  Tree-based algorithms such as random forests and gradient boosted trees
continue to be among the most popular and powerful machine learning models used
across multiple disciplines. The conventional wisdom of estimating the impact
of a feature in tree based models is to measure the \textit{node-wise reduction
of a loss function}, which (i) yields only global importance measures and (ii)
is known to suffer from severe biases. Conditional feature contributions (CFCs)
provide \textit{local}, case-by-case explanations of a prediction by following
the decision path and attributing changes in the expected output of the model
to each feature along the path. However, Lundberg et al. pointed out a
potential bias of CFCs which depends on the distance from the root of a tree.
The by now immensely popular alternative, SHapley Additive exPlanation (SHAP)
values appear to mitigate this bias but are computationally much more
expensive. Here we contribute a thorough comparison of the explanations
computed by both methods on a set of 164 publicly available classification
problems in order to provide data-driven algorithm recommendations to current
researchers. For random forests, we find extremely high similarities and
correlations of both local and global SHAP values and CFC scores, leading to
very similar rankings and interpretations. Analogous conclusions hold for the
fidelity of using global feature importance scores as a proxy for the
predictive power associated with each feature.
