Construct · 1 fields · 1 citations

selection bias in cross-validation: a standalone construct

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

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selection bias incross-validation1 CONSTRUCTSelection bias inAMBROISE · MACHINE LEARNING

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Machine learning

optimistic error estimates that result when feature selection or tuning uses the full data before cross-validation, so the test folds are not truly unseen.

In machine learning, selection bias in cross-validation refers to optimistic error estimates that result when feature selection or tuning uses the full data before cross-validation, so the test folds are not truly unseen. This meaning comes from Ambroise (2002) in Proceedings of the National Academy of Sciences.[1]

Unit
Recovery means
Measured by

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Nearby constructs

bias6 fields · 8 citationsjingle · one name, six constructscross-validation1 fields · 3 citationsanchor · a standalone constructerror4 fields · 8 citationsjingle · one word, four constructs

References

  1. Ambroise, C., & McLachlan, G. J. (2002). Selection bias in gene extraction on the basis of microarray gene-expression data. Proceedings of the National Academy of Sciences, 99(10), 6562-6566. https://doi.org/10.1073/pnas.102102699

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