The usual goal of supervised learning is to find the best model, the one that optimizes a particular performance measure. However, what if the explanation provided by this model is completely different from another model and different again from another model despite all having similarly good fit statistics? Is it possible that the equally effective models put the spotlight on different relationships in the data? Inspired by Anscombe's quartet, this paper introduces a Rashomon Quartet, i.e. a set of four models built on a synthetic dataset which have practically identical predictive performance. However, the visual exploration reveals distinct explanations of the relations in the data. This illustrative example aims to encourage the use of methods for model visualization to compare predictive models beyond their performance.

翻译：监督学习的常规目标是寻找最佳模型，即优化特定性能指标的模型。然而，如果该模型提供的解释与另一个模型截然不同，且与其他模型相比又呈现差异，尽管所有这些模型都具有相似的良好拟合统计量，情况会如何？是否可能存在这样的情况：同样有效的模型突出了数据中不同的关系？受安斯库姆四重奏的启发，本文引入了一个“拉什莫尔四重奏”，即基于合成数据集构建的一组四个模型，它们具有几乎相同的预测性能。然而，可视化探索揭示了数据关系中截然不同的解释。这一示例旨在鼓励使用模型可视化方法，将预测模型的比较扩展到性能之外。