Predictive models may generate biased predictions when classifying imbalanced datasets. This happens when the model favors the majority class, leading to low performance in accurately predicting the minority class. To address this issue, balancing or resampling methods are critical pre-processing steps in the modeling process. However, there have been debates and questioning of the functionality of these methods in recent years. In particular, many candidate models may exhibit very similar predictive performance, which is called the Rashomon effect, in model selection. Selecting one of them without considering predictive multiplicity which is the case of yielding conflicting models' predictions for any sample may lead to a loss of using another model. In this study, in addition to the existing debates, the impact of balancing methods on predictive multiplicity is examined through the Rashomon effect. It is important because the blind model selection is risky from a set of approximately equally accurate models. This may lead to serious problems in model selection, validation, and explanation. To tackle this matter, we conducted real dataset experiments to observe the impact of balancing methods on predictive multiplicity through the Rashomon effect. Our findings showed that balancing methods inflate the predictive multiplicity, and they yield varying results. To monitor the trade-off between performance and predictive multiplicity for conducting the modeling process responsibly, we proposed using the extended performance-gain plot for the Rashomon effect.

翻译：在对不平衡数据集进行分类时，预测模型可能产生有偏预测。当模型偏向多数类时，会导致对少数类准确预测的性能低下。为解决这一问题，平衡或重采样方法成为建模过程中至关重要的预处理步骤。然而，近年来对这些方法的功能性存在争议与质疑。特别是在模型选择中，许多候选模型可能表现出非常相似的预测性能，这种现象被称为拉什蒙效应。若在选择模型时不考虑预测多重性——即对于任何样本可能产生相互冲突的模型预测的情况，可能导致错失使用其他模型的机会。本研究在现有争议基础上，通过拉什蒙效应考察了平衡方法对预测多重性的影响。这一点至关重要，因为从一组预测精度相近的模型中进行盲目选择具有风险性，可能导致模型选择、验证与解释方面的严重问题。为探究此问题，我们通过真实数据集实验观察了平衡方法通过拉什蒙效应对预测多重性的影响。研究结果表明：平衡方法会加剧预测多重性，且产生的结果具有差异性。为在负责任地执行建模过程中权衡性能与预测多重性，我们提出了适用于拉什蒙效应的扩展性能增益图。