Abstract
OBJECTIVE: Methods to correct class imbalance (imbalance between the frequency of outcome events and nonevents) are receiving increasing interest for developing prediction models. We examined the effect of imbalance correction on the performance of logistic regression models. MATERIAL AND METHODS: Prediction models were developed using standard and penalized (ridge) logistic
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