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Given a labeled training set and a collection of unlabeled data, the goal ofactive learning (AL) is to identify the best unlabeled points to label. In thiscomprehensive study, we analyze the performance of a variety of AL algorithmson deep neural networks trained on 69 real-world tabular classificationdatasets from the OpenML-CC18 benchmark. We consider different data regimes andthe effect of self-supervised model pre-training. Surprisingly, we find thatthe classical margin sampling technique matches or outperforms all others,including current state-of-art, in a wide range of experimental settings. Toresearchers, we hope to encourage rigorous benchmarking against margin, and topractitioners facing tabular data labeling constraints thathyper-parameter-free margin may often be all they need.

Is margin all you need? An extensive empirical study of active learning on tabular data