Construct · 1 fields · 1 citations

benchmark label errors: a standalone construct

One name. One construct. A landmark on the map.

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Machine learning

mislabeled examples in widely used test sets, frequent enough to change which models rank highest when corrected.

In machine learning, benchmark label errors refers to mislabeled examples in widely used test sets, frequent enough to change which models rank highest when corrected. This meaning comes from Northcutt (2021) in arXiv.[1]

Unit
Recovery means
Measured by

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Nearby constructs

learning6 fields · 14 citationsjingle · one name, six constructs rank4 fields · 8 citationsjingle · one word, four constructs

References

Northcutt, C. G., Athalye, A., & Mueller, J. (2021). Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks. arXiv. https://doi.org/10.48550/arxiv.2103.14749

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