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.

通过 Rashomon 效应研究平衡方法对预测多样性的影响，发现平衡方法会增加预测多样性并产生不同结果。为了在建模过程中负责任地监控性能和预测多样性之间的权衡，我们提出使用扩展性能增益图进行模型选择。

不平衡分类中平衡方法的拉细幕效应实验研究

An Experimental Study on the Rashomon Effect of Balancing Methods in  Imbalanced Classification

The Rashomon Effect describes the following phenomenon: for a given dataset
there may exist many models with equally good performance but with different
solution strategies. The Rashomon Effect has implications for Explainable
Machine Learning, especially for the comparability of explanations. We provide
a unified view on three different comparison scenarios and conduct a
quantitative evaluation across different datasets, models, attribution methods,
and metrics. We find that hyperparameter-tuning plays a role and that metric
selection matters. Our results provide empirical support for previously
anecdotal evidence and exhibit challenges for both scientists and
practitioners.

本研究分析了 Rashomon 效应对可解释机器学习的影响，提供了三种不同比较场景的统一视角，并在不同数据集、模型、归因方法和指标上进行了定量评估，结果发现超参数调整和指标选择对结果有实质影响，并为科学家和实践者带来挑战。

解释性机器学习中罗生门效应的实证评估

An Empirical Evaluation of the Rashomon Effect in Explainable Machine  Learning

Here, I provide some reflections on Prof. Leo Breiman's "The Two Cultures"
paper. I focus specifically on the phenomenon that Breiman dubbed the "Rashomon
Effect", describing the situation in which there are many models that satisfy
predictive accuracy criteria equally well, but process information in the data
in substantially different ways. This phenomenon can make it difficult to draw
conclusions or automate decisions based on a model fit to data. I make
connections to recent work in the Machine Learning literature that explore the
implications of this issue, and note that grappling with it can be a fruitful
area of collaboration between the algorithmic and data modeling cultures.

本文针对 Leo Breiman 的文章《The Two Cultures》进行批判性反思，重点讨论了 Breiman 所谓的 “Rashomon Effect”，探究了许多模型可能有相同的预测精度，但处理数据方式却有很大差异，从而导致根据模型拟合数据得出的结论或自动决策变得困难的现象，同时与机器学习文献相连结并指出应该致力于算法和数据建模文化的合作。