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Visualization, visual analytics, and explainability

Visualization, visual analytics, and explainability Editorial Information Visualization 1–3 The Author(s) 2025 Visualization, visual analytics, Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/14738716251367674 and explainability journals.sagepub.com/home/ivi 1 2 3 Adrian Rusu , Nuno Datia ,KawaNazemi and Quang Vinh Nguyen in the ensemble models, which is important for identi- Visualization, visual analytics, and explainability are fying instances of overfitting and underfitting. As interconnected concepts that enhance understanding demonstrated in the case study and the user experi- and decision-making through data. Visualization ment, the interactive visualization can enhance the involves the graphical representation of data to reveal interpretability and adjustability of gradient boosting patterns, trends, and insights that might be difficult to decision trees, which is one of the most popular detect in raw numbers. Visual analytics combines instances of ensemble learning. automated analysis techniques with interactive visuali- Within the visual analytics and data science theme, zations, enabling users to explore complex datasets, Secco and Nazemi present a modular visual analytics generate hypotheses, and draw conclusions more system that uses transformer-based large language effectively. Explainability, in the context of machine models to automatically extract and classify clinical learning and AI, refers to the ability to interpret and entities from unstructured medical documents. This understand the decisions made by http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Information Visualization SAGE

Visualization, visual analytics, and explainability

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Publisher
SAGE
Copyright
© The Author(s) 2025
ISSN
1473-8716
eISSN
1473-8724
DOI
10.1177/14738716251367674
Publisher site
See Article on Publisher Site

Abstract

Editorial Information Visualization 1–3 The Author(s) 2025 Visualization, visual analytics, Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/14738716251367674 and explainability journals.sagepub.com/home/ivi 1 2 3 Adrian Rusu , Nuno Datia ,KawaNazemi and Quang Vinh Nguyen in the ensemble models, which is important for identi- Visualization, visual analytics, and explainability are fying instances of overfitting and underfitting. As interconnected concepts that enhance understanding demonstrated in the case study and the user experi- and decision-making through data. Visualization ment, the interactive visualization can enhance the involves the graphical representation of data to reveal interpretability and adjustability of gradient boosting patterns, trends, and insights that might be difficult to decision trees, which is one of the most popular detect in raw numbers. Visual analytics combines instances of ensemble learning. automated analysis techniques with interactive visuali- Within the visual analytics and data science theme, zations, enabling users to explore complex datasets, Secco and Nazemi present a modular visual analytics generate hypotheses, and draw conclusions more system that uses transformer-based large language effectively. Explainability, in the context of machine models to automatically extract and classify clinical learning and AI, refers to the ability to interpret and entities from unstructured medical documents. This understand the decisions made by

Journal

Information Visualization – SAGE

Published: Jan 1, 2025

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