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

Abstract

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

31 pages, 13 figures

References in corpus (4)

• To Explain or to Predict?
• Counterfactual Explanation Algorithms for Behavioral and Textual Data
• Explainable Text Classification in Legal Document Review A Case Study of Explainable Predictive Coding
• Explaining Classification Models Built on High-Dimensional Sparse Data

Cited by in corpus (1)

• Explainable AI for Psychological Profiling from Digital Footprints: A Case Study of Big Five Personality Predictions from Spending Data