Open Access

Evaluation metrics for rule learning typically, in one way or another, trade off consistency and coverage. In this work, we investigate this tradeoff for three different families of rule learning heuristics, all of them featuring a parameter that implements this trade-off in different guises. These heuristics are the m-estimate, the F-measure, and the Klösgen measures. The main goals of this work are to extend our understanding of these heuristics by visualizing their behavior via isometrics in coverage space, and to determine optimal parameter settings for them. Interestingly, even though the heuristics use quite different ways for implementing this trade-off, their optimal settings realize quite similar evaluation functions. Our empirical results on a large number of datasets demonstrate that, even though we do not use any form of pruning, the quality of the rules learned with these settings outperforms standard rule learning heuristics and approaches the performance of Ripper, a state-of-the-art rule learning system that uses extensive pruning and optimization phases.