通过径向可视化促进机器学习模型比较和解释
Facilitating Machine Learning Model Comparison and Explanation Through A Radial Visualisation
摘要：
为数据集构建有效的机器学习（ML）模型是一项涉及多个步骤的艰巨任务。最重要的步骤之一是比较生成的大量ML模型，以找到最适合部署的ML模型。这是具有挑战性的，比较这样的模型与动态数量的功能。比较不仅仅是发现ML模型性能的差异，用户还对特征和模型性能之间的关系感兴趣，例如ML解释的特征重要性。本文提出了RadialNet Chart，这是一种新的可视化方法，可以比较用给定数据集的不同数量的特征训练的ML模型，同时揭示隐含的依赖关系。在RadialNet Chart中，ML模型和特征分别由直线和圆弧表示。这些线是使用递归函数有效地生成的。具有动态特征数量的ML模型的依赖性被编码到可视化的结构中，其中ML模型及其依赖特征直接从相关的线连接中揭示。ML模型性能信息在RadialNet Chart中使用颜色和行宽进行编码。与可视化结构一起，特征重要性可以在RadialNet Chart中直接识别，用于ML解释。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
