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Understanding predictions made by Machine Learning models is critical in manyapplications. In this work, we investigate the performance of two methods forexplaining tree-based models- Tree Interpreter (TI) and SHapley AdditiveexPlanations TreeExplainer (SHAP-TE). Using a case study on detecting anomaliesin job runtimes of applications that utilize cloud-computing platforms, wecompare these approaches using a variety of metrics, including computationtime, significance of attribution value, and explanation accuracy. We findthat, although the SHAP-TE offers consistency guarantees over TI, at the costof increased computation, consistency does not necessarily improve theexplanation performance in our case study.

Evaluating Tree Explanation Methods for Anomaly Reasoning: A Case Study of SHAP TreeExplainer and TreeInterpreter