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When facing data with imbalanced classes or groups, practitioners follow anintriguing strategy to achieve best results. They throw away examples until theclasses or groups are balanced in size, and then perform empirical riskminimization on the reduced training set. This opposes common wisdom inlearning theory, where the expected error is supposed to decrease as thedataset grows in size. In this work, we leverage extreme value theory toaddress this apparent contradiction. Our results show that the tails of thedata distribution play an important role in determining theworst-group-accuracy of linear classifiers. When learning on data with heavytails, throwing away data restores the geometric symmetry of the resultingclassifier, and therefore improves its worst-group generalization.

Why does Throwing Away Data Improve Worst-Group Error?