OUCH: Oversampling and Undersampling Cannot Help Improve Accuracy in Our Bayesian Classifiers That Predict Preeclampsia

OUCH: Oversampling and Undersampling Cannot Help Improve Accuracy in Our Bayesian Classifiers That Predict Preeclampsia

Unbalanced data can have an impact on the machine learning (ML) algorithms that build predictive models. This manuscript studies the influence of oversampling and undersampling strategies on the learning of the Bayesian classification models that predict the risk of suffering preeclampsia. Given the...

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Main Authors: Franklin Parrales-Bravo, Rosangela Caicedo-Quiroz, Elena Tolozano-Benitez, Víctor Gómez-Rodríguez, Lorenzo Cevallos-Torres, Jorge Charco-Aguirre, Leonel Vasquez-Cevallos
Format: Article
Language:English
Published: MDPI AG 2024-10-01
Series:Mathematics
Subjects:
preeclampsia
bayesian network classifiers
class imbalance
oversampling
undersampling
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Online Access:https://www.mdpi.com/2227-7390/12/21/3351
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