Semisupervised Classifier Evaluation and Recalibration
This addresses the challenge of expensive labeling in data-rich environments, offering a method for efficient classifier evaluation and recalibration.
The paper tackles the problem of estimating classifier performance with limited labeled data by proposing Semisupervised Performance Evaluation (SPE), which uses a generative model for confidence scores to provide performance curves with confidence bounds using only a small number of labels.
How many labeled examples are needed to estimate a classifier's performance on a new dataset? We study the case where data is plentiful, but labels are expensive. We show that by making a few reasonable assumptions on the structure of the data, it is possible to estimate performance curves, with confidence bounds, using a small number of ground truth labels. Our approach, which we call Semisupervised Performance Evaluation (SPE), is based on a generative model for the classifier's confidence scores. In addition to estimating the performance of classifiers on new datasets, SPE can be used to recalibrate a classifier by re-estimating the class-conditional confidence distributions.