[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-242662-105":53,"doc-detail-242662-en":126},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":119,"head_meta":121,"extra_data":123,"updated_unix":125},105,"en","a-novel-hyperparameter-free-approach-to-decision-tree-construction-that-avoids-overfitting-by-design-abstract","A Novel Hyperparameter-free Approach to Decision Tree Construction that Avoids Overfitting by Design - Abstract","","Decision trees are an extremely popular machine learning technique, yet overfitting can limit performance and hinder adoption due to interpretability challenges. This work introduces a decision-tree construction method that avoids overfitting by design while preserving accuracy. The approach eliminates hyperparameter optimization and regularization, substantially reducing training time. It also yields significantly smaller, shallower trees, improving interpretability of the learned models.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":11,"@type":70,"position":76},"https://docshare.wps.com/template/presentations/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/a-novel-hyperparameter-free-approach-to-decision-tree-construction-that-avoids-overfitting-by-design-abstract/242662/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/a-novel-hyperparameter-free-approach-to-decision-tree-construction-that-avoids-overfitting-by-design-abstract/242662.png","ImageObject",442,249,{"name":88,"@type":89},"Dipper","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-11","2026-09-12",true,{"@type":98,"interactionType":99,"userInteractionCount":73},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"What problem does the paper address in decision tree learning?","Question",{"text":108,"@type":109},"The paper addresses overfitting in decision trees, which can reduce predictive performance on unseen data and make models difficult to interpret.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"How does the proposed approach avoid overfitting?",{"text":113,"@type":109},"It avoids overfitting by design while maintaining accuracy, and it does not rely on hyperparameter tuning or regularization techniques.",{"name":115,"@type":106,"acceptedAnswer":116},"What distinguishes the resulting decision trees from traditional methods?",{"text":117,"@type":109},"The algorithm produces much smaller and shallower trees than traditional approaches, which supports better interpretability.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},242662,1791724193,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":8,"category_name":11,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":79,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":135,"language":136,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":61,"update_tm":140,"read_time":79},1374404997633,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Received xxxxxx xx, xxxx, accepted xxxxxx xx, xxxx. Date of publication xxxx xx, xxxx, date of current version xxxx xx, xxxx.  \nDigital Object Identifier 10.1109/ACCESS.201x.xxxxxxx  \nA Novel Hyperparameter-free Approach to Decision Tree Construction that Avoids Overfitting by Design  \nRAFAEL GARCíA LEIVA, ANTONIO FERNANDEZ ANTA (Senior Member, IEEE), VINCENZO MANCUSO (Member, IEEE), PAOLO CASARI (Senior Member, IEEE)  \nIMDEA Networks Institute, 28918 Madrid, Spain  \nCorresponding author: Rafael García Leiva (e-mail: [rafael.garcia@imdea.org](rafael.garcia@imdea.org)).  \n ABSTRACT Decision trees are an extremely popular machine learning technique. Unfortunately, overfitting in decision trees still remains an open issue that sometimes prevents achieving good performance. In this work, we present a novel approach for the construction of decision trees that avoids the overfitting by design, without losing accuracy. A distinctive feature of our algorithm is that it requires neither the optimization of any hyperparameters, nor the use of regularization techniques, thus significantly reducing the decision tree training time. Moreover, our algorithm produces much smaller and shallower trees than traditional algorithms, facilitating the interpretability of the resulting models.  \n INDEX TERMS Decision Trees, Regularization, Interpretability, Kolmogorov Complexity  \nI. INTRODUCTION  \nDecision trees are a highly popular machine learning technique that has been successfully applied to solve a number of practical problems in areas such as natural language processing, information extraction, data mining, and pattern recognition [1] . Decision trees are also the foundation of more advanced machine learning methods, such as random forests or boosted trees [2] .  \nOne of the most challenging tasks for decision treebuilding algorithms is to decide when to stop the tree growing process. Most of the tree building methods produce very complex models that overfit training data. Overfitted trees not only have poor predictive capabilities on newpreviously unseen data, but also can be exceedingly difficult to interpret, which is a key barrier against the adoption of these models in practical applications [3] . A common approach to avoid overfitting in decision trees is early stopping, which forces the construction algorithm to stop before the tree becomes too complex. Popular stopping criteria include limiting the maximum depth of the tree, requiring a minimum number of sample points at leaf nodes, or computing the accuracy gain yielded by new nodes [1] . However, these heuristics  \nrequire the optimization of (possibly multiple) hyperparameters, which makes the training process computationally expensive.  \nAn alternative approach to avoid overfitting in decision trees is to apply a post-processing pruning algorithm that removes those branches that contribute the least to the accuracy of the final model [4] . Cost-complexity [2] is a common pruning technique, based on applying a cost function that takes into account how well the tree fits the training data anda regularization factor based on how complex the tree is. If we denote by E (T ) the fitness of a tree T, and by L (T ) its complexity, the cost function C (T ) could be given by  \nC (T ) = E(T ) + 􀀕L(T ); (1)  \nwhere 􀀕 is a configurable parameter that controls the trade-off between fitness and complexity. In practice, the accuracy of the model is typically used as a fitness metric, and the number of leaf nodes in the tree as a complexity metric. Although, on average, cost complexity pruning produces better results than limiting the growth of the tree via early stopping [5], this technique requires the optimization of the hyperparameter 􀀕 .  \nIn this paper we propose a decision tree construction algorithm with early stopping properties, that does not require hyperparameter tuning or pruning in order to get good accuracy and trees of small size.  \nA. RELATED WORK  \nEarlier algorithms for the construction of deci","cbCaib8YZYMxL7oS","https://ap.wps.com/l/cbCaib8YZYMxL7oS","pdf",488626,10,"English","# Introduction\n## Overfitting and early stopping\n## Pruning and cost-complexity\n# Related work","[{\"question\":\"What problem does the paper address in decision tree learning?\",\"answer\":\"The paper addresses overfitting in decision trees, which can reduce predictive performance on unseen data and make models difficult to interpret.\"},{\"question\":\"How does the proposed approach avoid overfitting?\",\"answer\":\"It avoids overfitting by design while maintaining accuracy, and it does not rely on hyperparameter tuning or regularization techniques.\"},{\"question\":\"What distinguishes the resulting decision trees from traditional methods?\",\"answer\":\"The algorithm produces much smaller and shallower trees than traditional approaches, which supports better interpretability.\"}]","A Novel Hyperparameter-free Approach to Decision Tree Construction that Avoids Overfitting by Design - Abstract | PDF",1789192975]