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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Notes
- 1.
Feature Attribution (FA) is defined as the contribution each independent variable or a “feature” made to the final prediction of a model.
- 2.
See Sect. 2 for the definition of consistency.
- 3.
Feature Attribution Method (FAM), referred to as the explanation method that calculates FAs to interpret each prediction generated by a model.
- 4.
Some of the covariate variables in postgreSQL dataset are continuous, which when grouped reduces the number of data points per cluster.
- 5.
RBO implementation: https://github.com/changyaochen/rbo.
- 6.
Dataset can be found at https://groups.cs.umass.edu/kdl/causal-eval-data.
- 7.
This data is collected in the work by [5].
- 8.
For eg, consider 2 lists of attribution values \(S_1=[1, 1.1, 1.3]\) and \(S_2=[1, 3, 5]\). The ranking obtained from values in \(S_2\) is more reliable than \(S_1\).
- 9.
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Acknowledgements
We thank our mentors, Javier Burroni and Prof. Andrew McCallum, for their guidance. We also thank Minsoo Thigpen for organizational support, as well as Scott Lundberg for providing insightful suggestions on a earlier draft. Finally, we thank anonymous reviewers for their feedback.
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Sharma, P. et al. (2020). Evaluating Tree Explanation Methods for Anomaly Reasoning: A Case Study of SHAP TreeExplainer and TreeInterpreter. In: Grossmann, G., Ram, S. (eds) Advances in Conceptual Modeling. ER 2020. Lecture Notes in Computer Science(), vol 12584. Springer, Cham. https://doi.org/10.1007/978-3-030-65847-2_4
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