Facilitating Machine Learning Model Comparison and Explanation Through A Radial Visualisation

TL;DR

This paper introduces RadialNet Chart, a novel visualization method that enables effective comparison of multiple machine learning models with varying features and reveals feature importance and relationships for better model understanding.

Contribution

The paper presents RadialNet Chart, a new visualization approach that simultaneously compares ML models with different features and uncovers feature-model dependencies for enhanced interpretability.

Findings

RadialNet Chart effectively visualizes models with varying features.

It reveals feature importance and dependencies clearly.

The approach improves model comparison and explanation.

Abstract

Building an effective Machine Learning (ML) model for a data set is a difficult task involving various steps. One of the most important steps is to compare generated substantial amounts of ML models to find the optimal one for the deployment. It is challenging to compare such models with dynamic number of features. Comparison is more than just finding differences of ML model performance, users are also interested in the relations between features and model performance such as feature importance for ML explanations. This paper proposes RadialNet Chart, a novel visualisation approach to compare ML models trained with a different number of features of a given data set while revealing implicit dependent relations. In RadialNet Chart, ML models and features are represented by lines and arcs respectively. These lines are generated effectively using a recursive function. The dependence of ML models with dynamic number of features is encoded into the structure of visualisation, where ML models and their dependent features are directly revealed from related line connections. ML model performance information is encoded with colour and line width in RadialNet Chart. Together with the structure of visualisation, feature importance can be directly discerned in RadialNet Chart for ML explanations.