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In various approaches to learning, notably in domain adaptation, activelearning, learning under covariate shift, semi-supervised learning, learningwith concept drift, and the like, one often wants to compare a baselineclassifier to one or more advanced (or at least different) strategies. In thischapter, we basically argue that if such classifiers, in their respectivetraining phases, optimize a so-called surrogate loss that it may also bevaluable to compare the behavior of this loss on the test set, next to theregular classification error rate. It can provide us with an additional view onthe classifiers' relative performances that error rates cannot capture. As anexample, limited but convincing empirical results demonstrates that we may beable to find semi-supervised learning strategies that can guarantee performanceimprovements with increasing numbers of unlabeled data in terms oflog-likelihood. In contrast, the latter may be impossible to guarantee for theclassification error rate.

On Measuring and Quantifying Performance: Error Rates, Surrogate Loss, and an Example in SSL