[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119314-en":3,"doc-seo-119314-105":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},119314,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",6,"Technology","Standard Decision Trees in Machine Learning - Master’s thesis - Explainability and H2O-3 implementation","Master’s thesis standardizes the Standard Decision Tree (SDT) algorithm and concentrates on practical approaches for creating SDTs with clear construction strategies. The work motivates SDTs as essential instruments for model explainability in Machine Learning by enabling transparent, rule-based reasoning. One construction method is selected and implemented in the H2O-3 Machine Learning platform, followed by testing on a suitable dataset. Results are then compared with an alternative ML model, assessing performance, scalability, and interpretability.","Assignment of master’s thesis  \nTitle: Standard Decision Trees in Machine Learning  \nStudent: Bc. Yuliia Syzon  \nSupervisor: Ing. Adam Valenta  \nStudy program: Informatics  \nBranch / specialization: Knowledge Engineering  \nDepartment: Department of Applied Mathematics  \nValidity: until the end of summer semester 2022/2023  \nInstructions  \nDescribe the Standard Decision Tree (SDT) algorithm. Focus on explaining the approaches to SDTs creation. Explain why SDTs are essential for the explainability of models in Machine Learning (ML). Select one appropriate method to construct SDT and implement it into the H2O-3 Machine Learning Platform. Test your implementation with a suitable dataset and compare it with another alternative ML model.  \nElectronically approved by Ing. Magda Friedjungová, Ph.D. on 21 April 2022 in Prague.  \nMaster’s thesis  \nSTANDARD DECISION TREES IN MACHINE LEARNING  \nBc. Yuliia Syzon  \nFaculty of Information Technology Department of Applied Mathematics Supervisor: Ing. Adam Valenta December 27, 2022  \nCzech Technical University in Prague Faculty of Information Technology  \n© 2023 Bc. Yuliia Syzon. All rights reserved.  \nThis thesis is school work as defined by Copyright Act of the Czech Republic. It has been submitted at Czech Technical University in Prague, Faculty of Information Technology. The thesis is protected by  \nthe Copyright Act and its usage without author’s permission is prohibited (with exceptions defined by the Copyright Act) .  \nCitation of this thesis: Syzon Yuliia. Standard Decision Trees in Machine Learning. Master’s thesis. Czech Technical University in Prague, Faculty of Information Technology, 2023 .  \nContents  \nAcknowledgments vi  \nDeclaration vii  \nAbstract viii  \nIntroduction 1  \n1 Research 3  \n1. 1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3  \n1.1.1 Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3  \n1.1.2 Decision Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6  \n1.1.3 H2O-3 Machine Learning platform ...................... 13  \n1.2 Approaches to Decision Tree creation ......................... 16  \n1.2.1 Standard Decision Tree (SDT) -Greedy approach ............. 18  \n1.2.2 Oblique decision trees   22  \n1.2.3 Evolutionary approach-genetic algorithms . . . . . . . . . . . . . . . . . 24  \n1.2.4 SAT-based Decision Tree Learning . . . . . . . . . . . . . . . . . . . . . . 28  \n1.2.5 Incremental approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30  \n1.3 ML models explainability with decision trees . . . . . . . . . . . . . . . . . . . . 32  \n1.3.1 Explanations via Surrogate Models . . . . . . . . . . . . . . . . . . . . . . 34  \n1.3.2 Neural-Backed Decision Trees . . . . . . . . . . . . . . . . . . . . . . . . . 36  \n2 Design of Single Decision Tree for H2O-3 39  \n2.1 Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39  \n2.1.1 Algorithm (R1 requirement) .......................... 40  \n2.1.2 Splitting rules (R2-R5 Requirements) .................... 40  \n2.1.3 Termination rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41  \n2.1.4 Assigning decision values to the leaves (R3, R6 requirements) ....... 41  \n2.1.5 Optimisations for huge datasets (R7 requirement) ............. 41  \n2.1.6 Distributed computation (R8 requirement) ................. 42  \n2.1.7 Presentability (R9 requirement) ........................ 42  \n2.2 Implementation notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43  \n3 Evaluation of DT implementation 45  \n3. 1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45  \n3.2 Comparing the results on data ............................ 46  \n3.3 Scalability ........................................ 47  \n3.4 Explainability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49  \n3.5 Drawbacks and future improvements   50","cbCaijBmvGscKpyf","https://ap.wps.com/l/cbCaijBmvGscKpyf","pdf",4709853,1,67,"English","en",105,"# Introduction\n# Research\n## Preliminaries\n## Approaches to Decision Tree creation\n## ML models explainability with decision trees\n# Design of Single Decision Tree for H2O-3\n## Requirements\n## Implementation notes\n# Evaluation of DT implementation\n## Datasets\n## Comparing the results on data\n## Scalability\n## Explainability\n## Drawbacks and future improvements\n# Conclusion","[{\"question\":\"What is the main focus of the thesis on Standard Decision Trees (SDT)?\",\"answer\":\"The thesis describes the SDT algorithm and emphasizes approaches to SDT creation, including design choices that affect how the trees are built and how they can be used for explanations.\"},{\"question\":\"Why are standard decision trees important for explainability in machine learning?\",\"answer\":\"Decision trees support transparent, rule-based decision paths, which helps explain model behavior in a way that is more interpretable than many black-box approaches.\"},{\"question\":\"How is the SDT method validated in the H2O-3 platform?\",\"answer\":\"A suitable SDT construction method is implemented in H2O-3, tested with an appropriate dataset, and then compared against another alternative ML model while evaluating results such as scalability and explainability.\"}]","Standard Decision Trees in Machine Learning - Master’s thesis - Explainability and H2O-3 implementation | PDF",1785723670,169,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"standard-decision-trees-in-machine-learning-masters-thesis-explainability-and-h2o-3-implementation","",{"@graph":36,"@context":85},[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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/standard-decision-trees-in-machine-learning-masters-thesis-explainability-and-h2o-3-implementation/119314/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main focus of the thesis on Standard Decision Trees (SDT)?","Question",{"text":75,"@type":76},"The thesis describes the SDT algorithm and emphasizes approaches to SDT creation, including design choices that affect how the trees are built and how they can be used for explanations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why are standard decision trees important for explainability in machine learning?",{"text":80,"@type":76},"Decision trees support transparent, rule-based decision paths, which helps explain model behavior in a way that is more interpretable than many black-box approaches.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the SDT method validated in the H2O-3 platform?",{"text":84,"@type":76},"A suitable SDT construction method is implemented in H2O-3, tested with an appropriate dataset, and then compared against another alternative ML model while evaluating results such as scalability and explainability.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,113,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]