EXTRACTION OF INTERPRETABLE DECISION RULES FROM BLACK-BOX MODELS FOR CLASSIFICATION TASKS

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2022-8-31

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GALATALI, EGEMEN BERK

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In this work, we have proposed a new method and ready to use workflow to extract simplified rule sets for a given Machine Learning (ML) model trained on a classifi- cation task. Those rules are both human readable and in the form of software code pieces thanks to the syntax of Python programming language. We have inspired from the power of Shapley Values as our source of truth to select most prominent features for our rule sets. The aim of this work to select the key interval points in given data in order to extract if-then rule sets representing the black box models. We are able to generate rules that can mimic four different ML models on three datasets and one Deep Learning model on stock price dataset. We have evaluated promising similarity scores (around 90%) between the extracted rules and the ML models.

Subject Keywords

classification, machine learning, explainability, Interpretability, Deep learning

URI

https://hdl.handle.net/11511/99494

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

E. B. GALATALI, “EXTRACTION OF INTERPRETABLE DECISION RULES FROM BLACK-BOX MODELS FOR CLASSIFICATION TASKS,” M.S. - Master of Science, Middle East Technical University, 2022.