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

arXiv: https://arxiv.org/abs/2003.04792 · HTML: https://mlsanity.com/paper/2003.04792/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2020.

## Built on by (most cited first, 2 of 2)

- [Towards Automated Error Analysis: Learning to Characterize Errors](https://mlsanity.com/paper/2201.05017/) (2022)
- [Explainable AI for Psychological Profiling from Digital Footprints: A Case Study of Big Five Personality Predictions from Spending Data](https://mlsanity.com/paper/2111.06908/) (2021)

## Abstract (arXiv)

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.