Data-driven advice for interpreting local and global model predictions in bioinformatics problems
This provides data-driven recommendations for researchers in bioinformatics and related fields using tree-based models, but it is incremental as it compares existing methods without introducing new ones.
The study compared two methods for interpreting tree-based model predictions, SHAP and conditional feature contributions (CFCs), across 164 classification problems, finding extremely high similarities and correlations in both local and global explanations, leading to very similar rankings and interpretations.
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.