  Predictive modelling is often reduced to finding the best model that
optimizes a selected performance measure. But what if the second-best model
describes the data in a completely different way? What about the third-best? Is
it possible that the equally effective models describe different relationships
in the data? Inspired by Anscombe's quartet, this paper introduces a Rashomon
quartet, a four models built on synthetic dataset which have practically
identical predictive performance. However, their visualization reveals distinct
explanations of the relation between input variables and the target variable.
The illustrative example aims to encourage the use of visualization to compare
predictive models beyond their performance.

Machine Learning;Machine Learning;Applications