Rethinking Streaming Machine Learning Evaluation

• URL: http://arxiv.org/abs/2205.11473v1
• Date: Mon, 23 May 2022 17:21:43 GMT
• Title: Rethinking Streaming Machine Learning Evaluation
• Authors: Shreya Shankar, Bernease Herman, Aditya G. Parameswaran
• Abstract summary: 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.
• Score: 9.69979862225396
• License: http://creativecommons.org/licenses/by/4.0/
• 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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