# On the Trade-off Between Consistency and Coverage in Multi-label Rule Learning Heuristics

## TL;DR

This paper investigates how the choice of heuristics in multi-label rule learning affects the trade-off between consistency and coverage, emphasizing the need for configurable learners to optimize different performance measures.

## Contribution

It highlights the importance of balancing consistency and coverage in multi-label rule learning heuristics and advocates for configurable learners tailored to specific performance measures.

## Key findings

- Trade-offs between consistency and coverage significantly impact multi-label prediction quality.
- Configurable learners can better optimize different multi-label performance measures.
- Local optimization of rules does not guarantee global measure maximization.

## Abstract

Recently, several authors have advocated the use of rule learning algorithms to model multi-label data, as rules are interpretable and can be comprehended, analyzed, or qualitatively evaluated by domain experts. Many rule learning algorithms employ a heuristic-guided search for rules that model regularities contained in the training data and it is commonly accepted that the choice of the heuristic has a significant impact on the predictive performance of the learner. Whereas the properties of rule learning heuristics have been studied in the realm of single-label classification, there is no such work taking into account the particularities of multi-label classification. This is surprising, as the quality of multi-label predictions is usually assessed in terms of a variety of different, potentially competing, performance measures that cannot all be optimized by a single learner at the same time. In this work, we show empirically that it is crucial to trade off the consistency and coverage of rules differently, depending on which multi-label measure should be optimized by a model. Based on these findings, we emphasize the need for configurable learners that can flexibly use different heuristics. As our experiments reveal, the choice of the heuristic is not straight-forward, because a search for rules that optimize a measure locally does usually not result in a model that maximizes that measure globally.