Amazing Things Come From Having Many Good Models

The Rashomon Effect , coined by Leo Breiman, describes the phenomenon that there exist many equally good predictive models for the same dataset. This phenomenon happens for many real datasets and when it does, it sparks both magic and consternation, but mostly magic. In light of the Rashomon Effect, this perspective piece proposes reshaping the way we think about machine learning, particularly for tabular data problems in the nondeterministic (noisy) setting. We address how the Rashomon Effect impacts (1) the existence of simple-yet-accurate models, (2) flexibility to address user preferences, such as fairness and monotonicity, without losing performance, (3) uncertainty in predictions, fairness, and explanations, (4) reliable variable importance, (5) al-gorithm choice, specifically, providing advanced knowledge of which algorithms might be suitable for a given problem, and (6) public policy. We also discuss a theory of when the Rashomon Effect occurs and why. Our goal is to illustrate how the Rashomon Effect can have a massive impact on the use of machine learning for complex problems in society.

由利奥·布雷曼（Leo Breiman）提出的“罗生门效应”描述了这样一种现象：对于同一数据集存在许多同样优秀的预测模型。这种现象在许多真实的数据集中都会出现，当它出现时，既会引发神奇感也会带来惊愕，但神奇感居多。鉴于罗生门效应，这篇观点文章提议重塑我们对机器学习的思考方式，特别是对于非确定性（有噪声）环境中的表格数据问题。我们阐述罗生门效应如何影响（1）简单且准确的模型的存在，（2）在不损失性能的情况下满足用户偏好（如公平性和单调性）的灵活性，（3）预测、公平性和解释中的不确定性，（4）可靠的变量重要性，（5）算法选择，具体而言，提供关于哪些算法可能适合给定问题的先验知识，以及（6）公共政策。我们还讨论了罗生门效应何时以及为何会发生的理论。我们的目标是说明罗生门效应如何对机器学习在社会复杂问题中的应用产生巨大影响。