风险预测模型的类不平衡校正的危害：使用逻辑斯谛回归的说明和模拟
The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression
摘要：
纠正类不平衡的方法，即结果事件和非事件的频率之间的不平衡，正在受到越来越多的关注，用于开发预测模型。我们研究了不平衡校正对标准和惩罚（岭）逻辑斯谛回归模型在区分、校准和分类方面的性能的影响。我们使用蒙特卡罗模拟和一个卵巢癌诊断的案例研究来检验随机欠采样、随机过采样和SMOTE。结果表明，所有的不平衡校正方法导致校准差（强烈高估的概率属于少数类），但没有更好的歧视在受试者工作特征曲线下的面积。不平衡校正在灵敏度和特异性方面改善了分类，但通过改变概率阈值获得了类似的结果。我们的研究表明，结果不平衡本身并不是一个问题，不平衡校正甚至可能会恶化模型的性能。
英文摘要：
Methods to correct class imbalance, i.e. imbalance between the frequency of outcome events and non-events, are receiving increasing interest for developing prediction models. We examined the effect of imbalance correction on the performance of standard and penalized (ridge) logistic regression models in terms of discrimination, calibration, and classification. We examined random undersampling, random oversampling and SMOTE using Monte Carlo simulations and a case study on ovarian cancer diagnosis. The results indicated that all imbalance correction methods led to poor calibration (strong overestimation of the probability to belong to the minority class), but not to better discrimination in terms of the area under the receiver operating characteristic curve. Imbalance correction improved classification in terms of sensitivity and specificity, but similar results were obtained by shifting the probability threshold instead. Our study shows that outcome imbalance is not a problem in itself, and that imbalance correction may even worsen model performance.
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