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arXiv · 2205.11473

Rethinking Streaming Machine Learning Evaluation

Shreya Shankar·Bernease Herman·Aditya G. Parameswaran
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Abstract

While most work on evaluating machine learning (ML) models focuses on computing accuracy on batches of data, tracking accuracy alone in a streaming setting (i.e., unbounded, timestamp-ordered datasets) fails to appropriately identify when models are performing unexpectedly. In this position paper, we discuss how the nature of streaming ML problems introduces new real-world challenges (e.g., delayed arrival of labels) and recommend additional metrics to assess streaming ML performance.

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Shreya Shankar, Bernease Herman, Aditya G. Parameswaran. 2022-05-23. Rethinking Streaming Machine Learning Evaluation. https://arxiv.org/abs/2205.11473

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