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- W4311832076 abstract "Interpretability is a pressing issue for decision systems. Many post hoc methods have been proposed to explain the predictions of a single machine learning model. However, business processes and decision systems are rarely centered around a unique model. These systems combine multiple models that produce key predictions, and then apply decision rules to generate the final decision. To explain such decisions, we propose the Semi-Model-Agnostic Contextual Explainer (SMACE), a new interpretability method that combines a geometric approach for decision rules with existing interpretability methods for machine learning models to generate an intuitive feature ranking tailored to the end user. We show that established model-agnostic approaches produce poor results on tabular data in this setting, in particular giving the same importance to several features, whereas SMACE can rank them in a meaningful way." @default.
- W4311832076 created "2022-12-31" @default.
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- W4311832076 date "2021-11-16" @default.
- W4311832076 modified "2023-10-16" @default.
- W4311832076 title "SMACE: A New Method for the Interpretability of Composite Decision Systems" @default.
- W4311832076 doi "https://doi.org/10.48550/arxiv.2111.08749" @default.
- W4311832076 hasPublicationYear "2021" @default.
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