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Interpreting predictions from tree ensemble methods such as gradient boostingmachines and random forests is important, yet feature attribution for trees isoften heuristic and not individualized for each prediction. Here we show thatpopular feature attribution methods are inconsistent, meaning they can lower afeature's assigned importance when the true impact of that feature actuallyincreases. This is a fundamental problem that casts doubt on any comparisonbetween features. To address it we turn to recent applications of game theoryand develop fast exact tree solutions for SHAP (SHapley Additive exPlanation)values, which are the unique consistent and locally accurate attributionvalues. We then extend SHAP values to interaction effects and define SHAPinteraction values. We propose a rich visualization of individualized featureattributions that improves over classic attribution summaries and partialdependence plots, and a unique "supervised" clustering (clustering based onfeature attributions). We demonstrate better agreement with human intuitionthrough a user study, exponential improvements in run time, improved clusteringperformance, and better identification of influential features. Animplementation of our algorithm has also been merged into XGBoost and LightGBM,see http://github.com/slundberg/shap for details.

Consistent Individualized Feature Attribution for Tree Ensembles