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

arXiv: https://arxiv.org/abs/2010.06734 · HTML: https://mlsanity.com/paper/2010.06734/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [Detection of Sensor-To-Sensor Variations using Explainable AI](https://mlsanity.com/paper/2306.10850/) (2023)
- [A Lightweight IDS for Early APT Detection Using a Novel Feature Selection Method](https://mlsanity.com/paper/2506.12108/) (2025)
- [Interpretable Model-Aware Counterfactual Explanations for Random Forest](https://mlsanity.com/paper/2510.27397/) (2025)

## Abstract (arXiv)

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.