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How many labeled examples are needed to estimate a classifier's performanceon a new dataset? We study the case where data is plentiful, but labels areexpensive. We show that by making a few reasonable assumptions on the structureof the data, it is possible to estimate performance curves, with confidencebounds, using a small number of ground truth labels. Our approach, which wecall Semisupervised Performance Evaluation (SPE), is based on a generativemodel for the classifier's confidence scores. In addition to estimating theperformance of classifiers on new datasets, SPE can be used to recalibrate aclassifier by re-estimating the class-conditional confidence distributions.

Semisupervised Classifier Evaluation and Recalibration