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Automatic Feasibility Study via Data Quality Analysis for ML: A Case-Study on Label Noise. (arXiv:2010.08410v4 [cs.LG] UPDATED)
Aug. 31, 2022, 1:10 a.m. | Cedric Renggli, Luka Rimanic, Luka Kolar, Wentao Wu, Ce Zhang
cs.LG updates on arXiv.org arxiv.org
In our experience of working with domain experts who are using today's AutoML
systems, a common problem we encountered is what we call "unrealistic
expectations" -- when users are facing a very challenging task with a noisy
data acquisition process, while being expected to achieve startlingly high
accuracy with machine learning (ML). Many of these are predestined to fail from
the beginning. In traditional software engineering, this problem is addressed
via a feasibility study, an indispensable step before developing any …
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