[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119715-en":3,"doc-seo-119715-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119715,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","StrategyAtlas - Strategy Analysis for Machine Learning Interpretability - Abstract","Businesses in high-risk environments hesitate to adopt modern machine learning because many models remain complex and hard to interpret. Existing methods mainly deliver local, instance-level explanations, which cannot fully capture global model behavior. The work proposes that strategy clusters—data groups treated distinctly by a model—can reveal global structure. STRATEGYATLAS is presented to analyze and explain these strategies, enabling simplification and improvements to reference models. A case with an insurance company demonstrates how data scientists improve production models using these insights.","StrategyAtlas: Strategy Analysis for Machine Learning Interpretability  \nCitation for published version (APA):  \nCollaris, D. , & van Wijk, J. J. (2023) . StrategyAtlas: Strategy Analysis for Machine Learning Interpretability. IEEE Transactions on Visualization and Computer Graphics, 29(6), 2996-3008.  \n[https://doi.org/10.1109/TVCG.2022.3146806](https://doi.org/10.1109/TVCG.2022.3146806)  \nDocument license:  \nCC BY  \nDOI:  \n10.1109/TVCG.2022.3146806  \nDocument status and date:  \nPublished: 01/06/2023  \nDocument Version:  \nPublisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)  \nPlease check the document version of this publication:  \n• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. 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Aug. 2026  \nStrategyAtlas: Strategy Analysis for Machine  \nLearning Interpretability  \nDennis Collaris  and Jarke J. van Wijk   \nAbstract—Businesses in high-risk environments have been reluctant to adopt modern machine learning approaches due to their complex and uninterpretable nature. Most current solutions provide local, instance-level explanations, but this is insufficient for understanding the model as a whole. In this work, we show that strategy clusters (i.e., groups of data instances that are treated distinctly by the model) can be used to understand the global behavior of a complex ML model. To support effective exploration and understanding of these clusters, we introduce STRATEGYATLAS, a system designed to analyze and explain model strategies. Furthermore, it supports multiple ways to utilize these strategies for simplifying and improving the reference model. In collaboration with a large insurance company, we present a use case in automatic insurance acceptance, and show how professional data scientists were enabled to understand a complex model and improve the production model based on these insights.  \nIndex Terms—Visual analytics, machine learning, explainable AI  \n~~ ~~ Ç ~~ ~~  \n1 INTRODUCTION  \nWHILE modern machine learning (ML) techniques have  \ngreat potential to solve a wide spectrum of real-world problems, some businesses have been reluctant to adopt this technology. Especially in high-risk environments, such as health care or the insurance sector, predictive performance alone is not sufficient. When critical decisions are made, we need to be able to hold ML models up to scrutiny. Either the mode","cbCaig7Y1vkJiBQE","https://ap.wps.com/l/cbCaig7Y1vkJiBQE","pdf",1430950,1,14,"English","en",105,"# Introduction\n## Global interpretation via strategy clusters\n## STRATEGYATLAS system and workflow\n## Use case in automatic insurance acceptance","[{\"question\":\"Why are machine learning models difficult to adopt in high-risk environments?\",\"answer\":\"Because many approaches are complex and not sufficiently interpretable, and predictive performance alone is not enough when critical decisions require scrutiny.\"},{\"question\":\"What problem with current explainability methods does this paper address?\",\"answer\":\"Most solutions provide only local, instance-level explanations, which are insufficient to understand the model as a whole.\"},{\"question\":\"How do strategy clusters help interpret a complex ML model?\",\"answer\":\"Strategy clusters group instances that the model treats distinctly, allowing users to analyze the model’s global behavior through the discovered strategies.\"}]","StrategyAtlas - 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