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In the Neyman-Pearson (NP) classification paradigm, the goal is to learn a classifier from labeled training data such that the probability of a false negative is minimized while the probability of a false positive is below a user-specified level α ∈ (0, 1). This work addresses the question of how to evaluate and compare classifiers in the NP setting. Simply reporting false positives and false negatives leaves some ambiguity about which classifier is best. Unlike conventional classification,doi:10.1109/tit.2007.901152 fatcat:mg6ys4geivepdghuzuamfrnkte