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By juxtaposing Taylor and Mutual Information diagrams, users can summarize performance, inspect linear and nonlinear relationships, and reveal uncertainty and other model properties. 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The traditional approach, which relies on presenting multi-model evaluation scores in the table, presents an obstacle when determining the similarities between the models and the order of performance.\u003Cbr>Methods: By combining statistics, information theory, and data visualization, juxtaposed Taylor and Mutual Information Diagrams permit users to track and summarize the performance of one model or a collection of different models. To uncover linear and nonlinear relationships between models, users may visualize one or both charts.\u003Cbr>Results: Our library presents the first publicly available implementation of the Mutual Information Diagram and its new interactive capabilities, as well as the first publicly available implementation of an interactive Taylor Diagram. Extensions have been implemented so that both diagrams can display temporality, multimodality, and multivariate data sets, and feature one scalar model property such as uncertainty. Our library, named polardiagrams, supports both continuous and categorical attributes.\u003Cbr>Conclusion: The library can be used to quickly and easily assess the performances of complex models, such as those found in machine learning, climate, or biomedical domains. |  |\n\n1. Introduction  \nOne of the last steps of any simulation or predictive analytics experiment is to determine the effectiveness of the used models and find the one that best explains the observed phenomenon. The visual comparison of one or two complex models containing multiple variables (dimensions) becomes impractical and often impossible when the number of dimensions exceeds three [1,2]. However, those models are standard in meteorological, medical, biological, and other similar domains. When considering more than two complex models, determining which model is the best becomes unachievable. Although we provide an in-depth examination of model interpretation in Section 4, it is imperative to note that any 􀀂-dimensional numerical vector is considered a model—hence the definition of a model is not restricted to a specific context within this paper.  \n* Corresponding author.  \nE-mail address: [aleksandar.anzel@uni-marburg.de](aleksandar.anzel@uni-marburg.de) (A. Anžel).  \nTo address the task of determining the best model, a quantification of the models’ quality is required. The related work relies on the observed data by calculating summary statistics or other types of measures (attributes). To present such attributes and statistics, visualization is needed. By visua","cbCaijObWqgCSPdL","https://ap.wps.com/l/cbCaijObWqgCSPdL","pdf",2610996,21,"English","# Introduction\n## Visual comparison challenges and dimensionality\n# Article sections","[{\"question\":\"What problem does the paper address in evaluating complex models?\",\"answer\":\"It addresses how hard it is to determine model similarities and performance order when multiple complex models and many evaluation metrics must be considered simultaneously.\"},{\"question\":\"How do the proposed diagrams help users compare models?\",\"answer\":\"By combining statistics, information theory, and data visualization, the approach uses Taylor and Mutual Information diagrams so users can track and summarize model performance and visualize linear and nonlinear relationships.\"},{\"question\":\"What is polardiagrams and what capabilities does it provide?\",\"answer\":\"polardiagrams is a library offering public implementations of interactive Mutual Information Diagrams and interactive Taylor Diagrams. It also supports temporality, multimodality, multivariate data sets, and can display scalar properties such as uncertainty.\"}]","Interactive polar diagrams for model comparison - Article - Objective, Methods, Results, Conclusion | PDF",53]