Learning good classifier models of rare events is a challenging task. On such problems, the recently proposed two-phase rule induction algorithm, PNrule, outperforms other non-meta methods of rule induction. Boosting is a strong meta-classifier approach, and has been shown to be adaptable to skewed class distributions. PNrule's key feature is to identify the relevant false positives and to collectively remove them. In this paper, we qualitatively argue that this ability is not guaranteed by the boosting methodology. We simulate learning scenarios of varying difficulty to demonstrate that this fundamental qualitative difference in the two mechanisms results in existence of many scenarios in which PNrule achieves comparable or significantly better performance than AdaCost, a strong cost-sensitive boosting algorithm. Even a comparable performance by PNrule is desirable because it yields a more easily interpretable model over an ensemble of models generated by boosting. We also show similar supporting results on real-world and benchmark datasets.