Are We Learning Yet? A Meta Review of Evaluation Failures Across Machine LearningDownload PDF

Published: 11 Oct 2021, Last Modified: 23 May 2023NeurIPS 2021 Datasets and Benchmarks Track (Round 2)Readers: Everyone
Keywords: evaluation, progress, benchmarks, meta-survey, meta-review, validity, transfer
TL;DR: We present a meta-review of evaluation failures across subfields of machine learning, finding surprisingly consistent failure modes.
Abstract: Many subfields of machine learning share a common stumbling block: evaluation. Advances in machine learning often evaporate under closer scrutiny or turn out to be less widely applicable than originally hoped. We conduct a meta-review of 107 survey papers from natural language processing, recommender systems, computer vision, reinforcement learning, computational biology, graph learning, and more, organizing the wide range of surprisingly consistent critique into a concrete taxonomy of observed failure modes. Inspired by measurement and evaluation theory, we divide failure modes into two categories: internal and external validity. Internal validity issues pertain to evaluation on a learning problem in isolation, such as improper comparisons to baselines or overfitting from test set re-use. External validity relies on relationships between different learning problems, for instance, whether progress on a learning problem translates to progress on seemingly related tasks.
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