Various ways to train and use bivalent classifiers are studied here in order to optimize the use of prevalence and misclassification cost information in classification tasks. Two things are demonstrated: (1) classifier performance is enhanced when these two factors are incorporated during training rather than during inferencing, and (2) probabilistic classification is usually more meaningful than pre-selected threshold rule classification.