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In the face of uncertainty, the need for probabilistic assessments has longbeen recognized in the literature on forecasting. In classification, however,comparative evaluation of classifiers often focuses on predictions specifying asingle class through the use of simple accuracy measures, which disregard anyprobabilistic uncertainty quantification. I propose probabilistic top lists asa novel type of prediction in classification, which bridges the gap betweensingle-class predictions and predictive distributions. The probabilistic toplist functional is elicitable through the use of strictly consistent evaluationmetrics. The proposed evaluation metrics are based on symmetric proper scoringrules and admit comparison of various types of predictions ranging fromsingle-class point predictions to fully specified predictive distributions. TheBrier score yields a metric that is particularly well suited for this kind ofcomparison.

From Classification Accuracy to Proper Scoring Rules: Elicitability of Probabilistic Top List Predictions