The Prediction Advantage: A Universally Meaningful Performance Measure for Classification and Regression
Abstract
We introduce the Prediction Advantage (PA), a novel performance measure for prediction functions under any loss function (e.g., classification or regression). The PA is defined as the performance advantage relative to the Bayesian risk restricted to knowing only the distribution of the labels. We derive the PA for wellknown loss functions, including 0/1 loss, crossentropy loss, absolute loss, and squared loss. In the latter case, the PA is identical to the wellknown Rsquared measure, widely used in statistics. The use of the PA ensures meaningful quantification of prediction performance, which is not guaranteed, for example, when dealing with noisy imbalanced classification problems. We argue that among several known alternative performance measures, PA is the best (and only) quantity ensuring meaningfulness for all noise and imbalance levels.
 Publication:

arXiv eprints
 Pub Date:
 May 2017
 arXiv:
 arXiv:1705.08499
 Bibcode:
 2017arXiv170508499E
 Keywords:

 Computer Science  Machine Learning