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On the Relationship between Data Efficiency and Error for Uncertainty Sampling
Stephen Mussmann · Percy Liang

Wed Jul 11 09:15 AM -- 12:00 PM (PDT) @ Hall B #128

While active learning offers potential cost savings, the actual data efficiency---the reduction in amount of labeled data needed to obtain the same error rate---observed in practice is mixed. This paper poses a basic question: when is active learning actually helpful? We provide an answer for logistic regression with the popular active learning algorithm, uncertainty sampling. Empirically, on 21 datasets from OpenML, we find a strong inverse correlation between data efficiency and the error rate of the final classifier. Theoretically, we show that for a variant of uncertainty sampling, the asymptotic data efficiency is within a constant factor of the inverse error rate of the limiting classifier.

Author Information

Stephen Mussmann (Stanford University)
Percy Liang (Stanford University)

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