• class prior estimation
• classifier accuracy prediction
• dataset shift
• learning to quantify
• machine learning
• prior probability shift
• quantification

Predicting the accuracy that a classifier will have on unseen data
(i.e., on unlabelled data that were not available at training time)
can be done via k-fold cross-validation (kFCV). However, using kFCV returns reliable predictions only when the training data and the unseen data are identically and independently distributed (IID), i.e., were randomly sampled from the same distribution. Unfortunately, in real-world applications it is often the case that the training data and the unseen data are not IID, i.e., that we want to deploy the trained model on unseen data that exhibit some kind of dataset shift with respect to the training data. In this work we deal with the problem of predicting classifier accuracy on unseen data characterised by prior probability shift (PPS), an important type of dataset shift. We propose a class of methods built on top of quantification algorithms robust to PPS, i.e., algorithms devised for estimating the prevalence values of the classes in unseen data characterised by PPS. The methods we propose are based on the idea of viewing the cells of the contingency table (on which classifier accuracy is computed) as classes. We perform systematic experiments in which we test the prediction accuracy of our methods against state-of-the-art classifier accuracy prediction methods from the machine learning literature.