Scalable visualisation methods for modern Generalized Additive Models

TL;DR

This paper introduces scalable, interactive visualization tools for Generalized Additive Models (GAMs) that leverage their additive structure, addressing current limitations in visualizing complex, large-scale GAMs in practical applications.

Contribution

The paper proposes a set of fast, scalable visualization methods for GAMs, implemented in the mgcViz R package, enhancing model development and presentation for complex, large datasets.

Findings

New visualization tools are fast enough for interactive use.

Tools effectively exploit the additive structure of GAMs.

Methods scale to large datasets and various response distributions.

Abstract

In the last two decades the growth of computational resources has made it possible to handle Generalized Additive Models (GAMs) that formerly were too costly for serious applications. However, the growth in model complexity has not been matched by improved visualisations for model development and results presentation. Motivated by an industrial application in electricity load forecasting, we identify the areas where the lack of modern visualisation tools for GAMs is particularly severe, and we address the shortcomings of existing methods by proposing a set of visual tools that a) are fast enough for interactive use, b) exploit the additive structure of GAMs, c) scale to large data sets and d) can be used in conjunction with a wide range of response distributions. All the new visual methods proposed in this work are implemented by the mgcViz R package, which can be found on the Comprehensive R Archive Network.