Facilitating Machine Learning Model Comparison and Explanation through a Radial Visualisation

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 numb...

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Autores principales:	Jianlong Zhou, Weidong Huang, Fang Chen
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	machine learning performance bar chart line chart radar chart RadialNet chart Technology T
Acceso en línea:	https://doaj.org/article/199da94ebdb740049c4d536c9aeae926
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spelling	oai:doaj.org-article:199da94ebdb740049c4d536c9aeae9262021-11-11T15:51:41ZFacilitating Machine Learning Model Comparison and Explanation through a Radial Visualisation10.3390/en142170491996-1073https://doaj.org/article/199da94ebdb740049c4d536c9aeae9262021-10-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/7049https://doaj.org/toc/1996-1073Building 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 <i>RadialNet Chart</i>, 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.Jianlong ZhouWeidong HuangFang ChenMDPI AGarticlemachine learningperformancebar chartline chartradar chartRadialNet chartTechnologyTENEnergies, Vol 14, Iss 7049, p 7049 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning performance bar chart line chart radar chart RadialNet chart Technology T
spellingShingle	machine learning performance bar chart line chart radar chart RadialNet chart Technology T Jianlong Zhou Weidong Huang Fang Chen Facilitating Machine Learning Model Comparison and Explanation through a Radial Visualisation
description	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 <i>RadialNet Chart</i>, 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.
format	article
author	Jianlong Zhou Weidong Huang Fang Chen
author_facet	Jianlong Zhou Weidong Huang Fang Chen
author_sort	Jianlong Zhou
title	Facilitating Machine Learning Model Comparison and Explanation through a Radial Visualisation
title_short	Facilitating Machine Learning Model Comparison and Explanation through a Radial Visualisation
title_full	Facilitating Machine Learning Model Comparison and Explanation through a Radial Visualisation
title_fullStr	Facilitating Machine Learning Model Comparison and Explanation through a Radial Visualisation
title_full_unstemmed	Facilitating Machine Learning Model Comparison and Explanation through a Radial Visualisation
title_sort	facilitating machine learning model comparison and explanation through a radial visualisation
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/199da94ebdb740049c4d536c9aeae926
work_keys_str_mv	AT jianlongzhou facilitatingmachinelearningmodelcomparisonandexplanationthrougharadialvisualisation AT weidonghuang facilitatingmachinelearningmodelcomparisonandexplanationthrougharadialvisualisation AT fangchen facilitatingmachinelearningmodelcomparisonandexplanationthrougharadialvisualisation
_version_	1718433289516089344

Facilitating Machine Learning Model Comparison and Explanation through a Radial Visualisation

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