Abstract: The performance of a classifier system may be limited due to the following: (1) nonmonotonic relationships between individual predictor cofactors and outcomes, (2) prevalence imbalances between development data and application environment data, and (3) failure to account for cost-gain economics. These issues are explored and statistically-based techniques for treating them are presented. In addition, probabilistic and fuzzy interpretations of classifier outputs are discussed, a likelihood ratio transformation of classifier outputs is suggested, and two new cost-gain indexes that rate classifier systems in global economic terms are introduced.