[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120547-en":3,"doc-seo-120547-105":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},120547,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Adapting performance metrics for ordinal classification to interval scale - length matters","Accurate evaluation of supervised classification models determines both performance assessment and model selection quality. This paper studies ordinal classification and develops ways to adapt ordinal performance metrics to the interval scale, where measurements are taken in intervals whose lengths carry meaning. It revisits two confusion-matrix-based ordinal metrics, introduces normalization for comparability, and builds a robust framework for interval-scale adaptation. The work tackles the unbounded rightmost interval challenge through theory and simulations and validates results with experiments on real-world datasets. Findings indicate interval-scale metrics can effectively support hyper-parameter tuning to improve model performance.","Adapting performance metrics for ordinal classification to interval scale: length matters  \nGiulia Binotto1 · Rosario Delgado1  \nReceived: 16 May 2024 / Revised: 18 November 2024 / Accepted: 19 December 2024 /  \nPublished online: 27 January 2025 © The Author(s) 2025  \nAbstract  \nIn the field of supervised machine learning, accurate evaluation of classification models is a critical factor for assessing their performance and guiding model selection. This paper delves into the domain of ordinal classification and raises the question of adapting ordinal metrics to the interval scale. In scenarios where measurements are recorded at intervals, not only the order but also their length assume significance, and this promotes the adoption of novel performance metrics. Initially, we revisit two existing confusion matrix-based ordinal metrics and introduce a normalization technique to render them comparable and enhance their practical utility. We extend our focus to classification by intervals, proposing a robust framework for adapting ordinal metrics to the interval scale, and applying it to the aforementioned ordinal metrics. We address the challenge of unbounded rightmost intervals, a common issue in practical applications, from both theoretical and simulation perspectives, by providing a solution that enhances the applicability of the proposed metrics. To further explore practical implications, we conducted experiments on real-world datasets. The results reveal a promising trend in the use of interval-scale metrics to guide hyper-parameter tuning for improving model performance.  \nKeywords Ordinal classification · Interval-scale classification · Performance metrics · Cost-sensitive metrics · Hyper-parameter tuning  \nEditor: Willem Waegeman.  \n* Giulia Binotto  \n[Giulia.Binotto@uab.cat](Giulia.Binotto@uab.cat)  \nRosario Delgado  \n[Rosario.Delgado@uab.cat](Rosario.Delgado@uab.cat)  \n1 Department of Mathematics, Autonomous University of Barcelona UAB, Av. de l’Eix Central s/n, 08193 Cerdanyola del Vallès, Spain  \n1 Introduction  \nClassification is one of the tasks in supervised machine learning. Its primary objective is to predict the suitable class or label for given input data, accomplished through the utilization of a predictive model, commonly referred to as a classifier. Model validation is performed using a previously unseen test data set: by comparing the predicted labels to the actual observations within the test set, a relevant metric can be formulated and employed to assess the predictive efficacy of the model.  \nIn multi-class classification, where labels lack a natural ordering, a nominal scale is employed. Examples include car brand, type of job, political party, pets, sport, and more. However, in cases where classes possess an inherent ordering, and only their rank matters, we use an ordinal scale. For instance, when gauging customers’ opinions in online shopping, we might consider categories like would not recommend, would recommend, or would highly recommend. This exemplifies a Likerttype scale, typically comprising 3, 5, or 7 points that respondents use to express their level of agreement or disagreement with a statement. Other examples include completely disagree, disagree, neutral, agree and completely agree, or always, often and sometimes. This scale was introduced by Likert (1932) and is widely employed in sentiment analysis, satisfaction surveys, opinion mining, and, more recently, information retrieval processes within the context of recommender systems (Pang and Lee, 2008) . Responses on an ordinal scale can be scored or ranked, but the gaps between categories are not quantifiable. In other words, using an example, it isnot meaningful to compare the “distance” between often and always with the “distance” between often and sometimes.  \nAfter the nominal and ordinal scales, the interval scale represents the third level of measurement. The interval scale is an ordered quantitative measurement scale that records measu","cbCaigLSYN8iiYCO","https://ap.wps.com/l/cbCaigLSYN8iiYCO","pdf",3429988,1,49,"English","en",105,"# Introduction\n## Measurement scales: nominal, ordinal, and interval\n## Why interval length matters for evaluation\n# Method and framework\n## Revisiting confusion-matrix-based ordinal metrics\n## Normalization to compare metrics\n## Adapting metrics to interval-scale classification\n## Handling unbounded rightmost intervals\n# Experiments and results\n## Experiments on real-world datasets\n## Impact on hyper-parameter tuning","[{\"question\":\"What is the key problem addressed in this paper?\",\"answer\":\"Adapting performance metrics used for ordinal classification so they correctly reflect evaluation on an interval scale, where interval length influences the meaning of errors.\"},{\"question\":\"How does the paper improve comparability between existing ordinal metrics?\",\"answer\":\"It revisits two confusion-matrix-based ordinal metrics and introduces a normalization technique so they become comparable and more useful in practice.\"},{\"question\":\"How does the proposed approach handle the unbounded rightmost interval issue?\",\"answer\":\"It provides a solution supported by theoretical reasoning and simulations to improve the applicability of the interval-scale-adapted metrics in real settings.\"}]","Adapting performance metrics for ordinal classification to interval scale - 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