Semisupervised Classifier Evaluation and Recalibration
Machine Learning
2012-10-09 v1 Computer Vision and Pattern Recognition
Abstract
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.
Cite
@article{arxiv.1210.2162,
title = {Semisupervised Classifier Evaluation and Recalibration},
author = {Peter Welinder and Max Welling and Pietro Perona},
journal= {arXiv preprint arXiv:1210.2162},
year = {2012}
}