2008
NeurIPS
NeurIPS 2008
On Bootstrapping the ROC Curve
Abstract
This paper is devoted to thoroughly investigating how to bootstrap the ROC curve, a widely used visual tool for evaluating the accuracy of test/scoring statistics in the bipartite setup. The issue of confidence bands for the ROC curve is considered and a resampling procedure based on a smooth version of the empirical distribution called the smoothed bootstrap" is introduced. Theoretical arguments and simulation results are presented to show that the "smoothed bootstrap" is preferable to a "naive" bootstrap in order to construct accurate confidence bands."
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Topics
Machine Learning > Core Methods > Classification
Machine Learning > Optimization & Theory > Theory
Mathematics & Optimization > Mathematics > Statistics
Mathematics & Optimization > Statistics
Machine Learning > Optimization & Theory > Statistics
Machine Learning > Learning Types > Evaluation
Machine Learning > Core Methods > Estimation