Debiasing SHAP scores in random forests
- AbstractBlack box machine learning models are currently being used for high-stakes decision making in various parts of society such as healthcare and criminal justice. While tree-based ensemble methods such as random forests typically outperform deep learning models on tabular data sets, their built-in variable importance algorithms are known to be strongly biased toward high-entropy features. It was recently shown that the increasingly popular SHAP (SHapley Additive exPlanations) values suffer from a similar bias. We propose debiased or "shrunk" SHAP scores based on sample splitting which additionally enable the detection of overfitting issues at the feature level.
Author: | Markus LoecherORCiD |
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URN: | urn:nbn:de:kobv:b721-opus4-42445 |
DOI: | https://doi.org/10.1007/s10182-023-00479-7 |
ISSN: | 1863-8171 |
Parent Title (English): | AStA Advances in Statistical Analysis |
Publisher: | Springer Science and Business Media LLC |
Document Type: | Article |
Language: | English |
Year of Completion: | 2023 |
Year of first Publication: | 2023 |
Publishing Institution: | Hochschulbibliothek HWR Berlin |
Release Date: | 2023/09/06 |
Tag: | Analysis; Applied Mathematics; Economics and Econometrics; Modeling and Simulation; Social Sciences (miscellaneous); Statistics and Probability |
Institutes: | FB I - Wirtschaftswissenschaften |
Open Access Publikationen (DINI-Set): | open_access |
Open Access Publikations financed by DEAL Project: | hybrid |
Licence (German): | Creative Commons - CC BY - Namensnennung 4.0 International |