Impact of random oversampling and random undersampling on the performance of prediction models developed using observational health data
Abstract Background There is currently no consensus on the impact of class imbalance methods on the performance of clinical prediction models. We aimed to empirically investigate the impact of random oversampling and random undersampling, two commonly used class imbalance methods, on the internal an...
Main Authors: | , , , , |
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Format: | Article |
Language: | English |
Published: |
SpringerOpen
2024-01-01
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Series: | Journal of Big Data |
Subjects: | |
Online Access: | https://doi.org/10.1186/s40537-023-00857-7 |