Understanding covariate shift in model performance [version 2; peer review: 1 approved, 1 approved with reservations]

Three (3) different methods (logistic regression, covariate shift and k-NN) were applied to five (5) internal datasets and one (1) external, publically available dataset where covariate shift existed. In all cases, k-NN's performance was inferior to either logistic regression or covariate shift...

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Veröffentlicht in:F1000 research 2016, Vol.5, p.597
Hauptverfasser: McGaughey, Georgia, Walters, W. Patrick, Goldman, Brian
Format: Artikel
Sprache:eng
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Zusammenfassung:Three (3) different methods (logistic regression, covariate shift and k-NN) were applied to five (5) internal datasets and one (1) external, publically available dataset where covariate shift existed. In all cases, k-NN's performance was inferior to either logistic regression or covariate shift. Surprisingly, there was no obvious advantage for using covariate shift to reweight the training data in the examined datasets.
ISSN:2046-1402
2046-1402
DOI:10.12688/f1000research.8317.2