Performance Is Not Enough: The Story Told by a Rashomon Quartet
本文通过一个合成数据集构建了四个预测性能几乎相同但解释截然不同的模型（拉什莫尔四重奏），说明仅靠性能指标不足以理解模型，鼓励使用可视化方法比较模型。
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 article introduces a Rashomon Quartet, that is 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.