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

• URL: http://arxiv.org/abs/2010.06734v1
• Date: Tue, 13 Oct 2020 23:18:26 GMT
• Title: Evaluating Tree Explanation Methods for Anomaly Reasoning: A Case Study of SHAP TreeExplainer and TreeInterpreter
• Authors: Pulkit Sharma, Shezan Rohinton Mirzan, Apurva Bhandari, Anish Pimpley, Abhiram Eswaran, Soundar Srinivasan and Liqun Shao
• Abstract summary: We investigate the performance of two methods for explaining tree-based models- Tree Interpreter (TI) and SHapley Additive exPlanations TreeExplainer (SHAP-TE) 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.
• Score: 6.718611456024702
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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