[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124092-en":3,"doc-seo-124092-105":30,"detail-sidebar-cat-0-en-105":83},{"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},124092,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models","A model-agnostic framework is presented for jointly optimizing predictive performance and interpretability in supervised machine learning for tabular data. Interpretability is quantified with three sparsity-based measures: feature sparsity, interaction sparsity, and sparsity of non-monotone feature effects. Hyperparameter tuning is reformulated as a multi-objective optimization task, enabling diverse models that trade off accuracy and interpretability in a single run. Search efficiency is improved by augmenting the hyperparameter space with feature selection, interaction, and monotonicity constraints, supported by an evolutionary algorithm and validated on benchmarks against XGBoost and Explainable Boosting Machine.","Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models  \nLennart Schneider  \nLMU Munich & Munich Center for Machine Learning (MCML) Munich, Germany lennart.schneider@stat.uni[muenchen.de](muenchen.de)  \nBernd Bischl  \nLMU Munich & Munich Center for Machine Learning (MCML) Munich, Germany [bernd.bischl@stat.uni-muenchen.de](bernd.bischl@stat.uni-muenchen.de)  \nJanek Thomas  \nLMU Munich & Munich Center for Machine Learning (MCML) Munich, Germany  \n[janek.thomas@stat.uni-muenchen.de](janek.thomas@stat.uni-muenchen.de)  \nABSTRACT  \nWe present a model-agnostic framework for jointly optimizing the predictive performance and interpretability of supervised machine learning models for tabular data. Interpretability is quantified via three measures: feature sparsity, interaction sparsity of features, and sparsity of non-monotone feature effects. By treating hyperparameter optimization of a machine learning algorithm asa multi-objective optimization problem, our framework allows for generating diverse models that trade off high performance and ease of interpretability in a single optimization run. Efficient optimization is achieved via augmentation of the search space of the learning algorithm by incorporating feature selection, interaction and monotonicity constraints into the hyperparameter search space. We demonstrate that the optimization problem effectively translates to finding the Pareto optimal set of groups of selected features that are allowed to interact in a model, along with finding their optimal monotonicity constraints and optimal hyperparameters of the learning algorithm itself. We then introduce a novel evolutionary algorithm that can operate efficiently on this augmented search space. In benchmark experiments, we show that our framework is capable of finding diverse models that are highly competitive or outperform state-of-the-art XGBoost or Explainable Boosting Machine models, both with respect to performance and interpretability.  \nCCS CONCEPTS  \n• Computing methodologies → Supervised learning; Featureselection.  \nKEYWORDS  \nsupervised learning, performance, interpretability, tabular data, multi-objective, evolutionary computation, group structure  \nACM Reference Format:  \nLennart Schneider, Bernd Bischl, and Janek Thomas. 2023. Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models. In Genetic and Evolutionary Computation Conference (GECCO’23), July 15–19, 2023, Lisbon, Portugal. ACM, New York, NY, USA, 10 pages. [https://doi.org/10.1145/3583131.3590380](https://doi.org/10.1145/3583131.3590380)  \nThis work is licensed under a Creative Commons Attribution International 4.0 License.  \nGECCO’23, July 15–19, 2023, Lisbon, Portugal © 2023 Copyright held by the owner/author(s) .  \nACM ISBN 979-8-4007-0119-1/23/07 .  \n[https://doi.org/10.1145/3583131.3590380](https://doi.org/10.1145/3583131.3590380)  \n1 INTRODUCTION  \nTabular data are highly relevant for numerous application areas such as finance, bio-informatics, and medical diagnosis. State-of-theart learning algorithms for tabular data include tree-based methods, e.g., gradient boosted trees (with larger depth) [20] such as XGBoost [8] and LightGBM [33], or random forests [6], which often still outperform deep neural networks [25], although the performance gap has recently shrunk considerably [23, 25, 31, 50] . To achieve peak predictive performance, AutoML tools such as AutoGluon-Tabular [15] or AutoSklearn [19] often make further use of ensembling and stacking multiple models. Moreover, careful hyperparameter optimization of learning algorithms is typically required to yield well performing models [47, 52] .  \nWhile good predictive performance is generally of central importance, many applications desire or even require models to fulfill additional criteria, such as interpretability or sparseness. For example a model used for medical diagnosis that achieves hi","cbCaiaGrbMXRjlaC","https://ap.wps.com/l/cbCaiaGrbMXRjlaC","pdf",781570,1,10,"English","en",105,"# Abstract\n# Introduction\n## Tabular data and learning algorithms\n## Interpretability requirements and approaches\n## Motivation for multi-criteria model selection","[{\"question\":\"How do the experimental results compare to existing methods?\",\"answer\":\"Benchmarks show the approach can find diverse models that are competitive with or outperform state-of-the-art XGBoost or Explainable Boosting Machine models in both performance and interpretability.\"}]","Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models | PDF",1785820283,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":28},"multi-objective-optimization-of-performance-and-interpretability-of-tabular-supervised-machine-learning-models","",{"@graph":36,"@context":77},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/multi-objective-optimization-of-performance-and-interpretability-of-tabular-supervised-machine-learning-models/124092/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"How do the experimental results compare to existing methods?","Question",{"text":75,"@type":76},"Benchmarks show the approach can find diverse models that are competitive with or outperform state-of-the-art XGBoost or Explainable Boosting Machine models in both performance and interpretability.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,126],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":21,"slug":125},"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":98,"slug":129},19,"General","general"]