Performance is not enough: the story told by a Rashomon quartet

Abstract

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.

References in corpus (4)

• All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously
• Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process
• The Grammar of Interactive Explanatory Model Analysis
• Exploration of the Rashomon Set Assists Trustworthy Explanations for Medical Data

Cited by in corpus (1)

• Birds look like cars: Adversarial analysis of intrinsically interpretable deep learning