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Machine learning models on behavioral and textual data can result in highlyaccurate prediction models, but are often very difficult to interpret.Rule-extraction techniques have been proposed to combine the desired predictiveaccuracy of complex "black-box" models with global explainability. However,rule-extraction in the context of high-dimensional, sparse data, where manyfeatures are relevant to the predictions, can be challenging, as replacing theblack-box model by many rules leaves the user again with an incomprehensibleexplanation. To address this problem, we develop and test a rule-extractionmethodology based on higher-level, less-sparse metafeatures. A key finding ofour analysis is that metafeatures-based explanations are better at mimickingthe behavior of the black-box prediction model, as measured by the fidelity ofexplanations.

Metafeatures-based Rule-Extraction for Classifiers on Behavioral and Textual Data