Facilitating Machine Learning Model Comparison and Explanation through a Radial Visualisation

• Published in: Energies (Basel) 2021-11, Vol.14 (21), p.7049
• Main Authors: Zhou, Jianlong, Huang, Weidong, Chen, Fang
• Format: Article
• Language: English
• Subjects: Algorithms; bar chart; Big Data; Datasets; Learning algorithms; line chart; Machine learning; radar chart; RadialNet chart; Recursive functions; Visual perception; Visualization
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en14217049

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 a substantial amount of generated ML models to find the optimal one for deployment. It is challenging to compare such models with a dynamic number of features. Comparison is more than only finding differences of ML model performance, as 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 a 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. Taken together with the structure of visualisation, feature importance can be directly discerned in RadialNet Chart for ML explanations. Compared with other commonly used visualisation approaches, RadialNet Chart can help to simplify the ML model comparison process with different benefits such as the following: more efficient in terms of helping users to focus their attention to find visual elements of interest and easier to compare ML performance to find optimal ML model and discern important features visually and directly instead of through complex algorithmic calculations for ML explanations.