Understanding predictions made by Machine Learning models is critical in many applications. In this work, we investigate the performance of two methods for explaining tree-based models- Tree Interpreter (TI) and SHapley Additive exPlanations TreeExplainer (SHAP-TE). Using a case study on detecting anomalies in job runtimes of applications that utilize cloud-computing platforms, we compare these approaches using a variety of metrics, including computation time, significance of attribution value, and explanation accuracy. We find that, although the SHAP-TE offers consistency guarantees over TI, at the cost of increased computation, consistency does not necessarily improve the explanation performance in our case study.
@article{arxiv.2010.06734,
title = {Evaluating Tree Explanation Methods for Anomaly Reasoning: A Case Study of SHAP TreeExplainer and TreeInterpreter},
author = {Pulkit Sharma and Shezan Rohinton Mirzan and Apurva Bhandari and Anish Pimpley and Abhiram Eswaran and Soundar Srinivasan and Liqun Shao},
journal= {arXiv preprint arXiv:2010.06734},
year = {2020}
}