A Radial Visualisation for Model Comparison and Feature Identification

Abstract

Machine Learning (ML) plays a key role in various intelligent systems, and building an effective ML model for a data set is a difficult task involving various steps including data cleaning, feature definition and extraction, ML algorithms development, model training and evaluation as well as others. One of the most important steps in the process 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. This paper proposes a novel visualisation approach based on a radial net to compare ML models trained with a different number of features of a given data set while revealing implicit dependent relations. In the proposed approach, ML models and features are represented by lines and arcs respectively. 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 the innovative visualisation. Together with the structure of visualization, feature importance can be directly discerned to help to understand ML models.