  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.
