[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127943-en":3,"doc-seo-127943-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127943,687207024478,"Liam","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","OPTIMISING MACHINE LEARNING TECHNIQUES - AUTOFLEX: AN AUTOML APPROACH - Master Thesis","Automated Machine Learning (AutoML) addresses key bottlenecks in machine learning, including algorithm selection, hyperparameter optimisation, feature engineering, scalability, and interpretability. This master thesis reviews and evaluates state-of-the-art AutoML frameworks and techniques by scrutinising strengths, limitations, and domain applicability. Comparisons cover model performance, execution time, and interpretability to clarify trade-offs. Based on the findings, a novel AutoFlex approach is proposed, integrating established algorithms with automated pre-processing to improve input quality and maintain interpretability. Extensive experiments across diverse datasets analyse performance–time–interpretability relationships and support further research to maximise AutoML’s potential for complex tasks.","EuropeW/00Jf(ClwT23) 􀂗  \nHandelsh0ysllolen Bl GRA 19703 Master Thesis  \nThesis Master of Science 100% - W  \nPredefinert informasjon  \nStartdato:  \nSluttdato:  \nEllsamensform:  \nFlowkode:  \nIntern sensor:  \nDelta􀂘er  \nNavn:  \n09-01-2023 09:00 CET  \n03-07-2023 12:00 CESTT 20231011111841IINOOIIWIIT (Anonymisert)  \nSai Prudhvi Neelakantam  \nTermin:  \nVurderingsform:  \n202310  \nNorsk 6-trinns sllala (A-F)  \nlnformasjon fra delta􀂘er  \nTittel •: OPTIMISING MACHINE LEARNING TECHNIQUES: AUTOFLEX-AN AUTOML APPROACH  \nNaun pli ueileder •: WEI TING YANG  \nlnneholder besuarelsen Nei konfidensielt  \nmateriale7:  \nKan besuarelsenoffentliggj•res?:  \nJa  \nGruppe  \nGruppenaun: (Anonymisert)  \n(jruppenummer: 217  \nAndre medlemmer i Deltakeren har innleuert i en enlleltmannsgruppe gruppen:  \nMASTER THESIS  \nOPTIMISING MACHINE LEARNING TECHNIQUES: AUTOFLEX – AN AUTOML APPROACH  \nHand-in date  \n03.07.2023  \nCampus  \nBI OSLO  \nSupervisor  \nWEI TING YAN  \nProgramme  \nMASTER OF SCIENCE IN BUSINESS ANALYTICS  \nABSTRACT  \nAutomated Machine Learning (AutoML) has emerged as a promising solution to tackle the challenges of algorithm selection, hyperparameter optimisation, feature engineering, scalability, and interpretability in ML tasks. This master thesis aims to profoundly investigate the state-of-the-art in AutoML and suggest revolutionary approaches to enhance its capabilities.  \nThe research begins with a thorough review and evaluation of existing AutoML frameworks and techniques. Strengths, limitations, and applicability are scrutinised, providing valuable insights into their performance and usability across different problem domains. The evaluation includes comparisons of model performance, execution time, and interpretability, enhancing the understanding of the trade-offs involved.  \nBased on the findings, a novel approach, AutoFlex, is proposed to integrate established algorithms with automated pre-processing techniques. This approach leverages algorithms such as Random Forest Classifier, Gradient Boosting Regressor, and Decision Tree Classifier to ensure model interpretability. Additionally, pre-processing techniques like StandardScaler, RobustScaler, and OneHotEncoder are developed to enhance the quality of input data.  \nExtensive experiments are conducted on diverse datasets to evaluate the performance and interpretability of the proposed approach. Visualisations and analysis provide insights into the relationship between model performance, execution time, and interpretability, helping to interpret experimental findings.  \nThe proposed approach, AutoFlex, combines interpretable algorithms with automated pre-processing techniques, which is crucial to developing more effective and usable AutoML systems. As AutoML continues to evolve, further research and advancements are necessary to address its limitations and maximise its potential for tackling complex ML tasks. We must continuously explore and innovate AutoML to ensure it remains a reliable and safe solution for various applications.  \nACKNOWLEDGEMENTS  \nI want to express my deepest gratitude to Wei Ting Yan, my master’s thesis Supervisor from BI Norwegian Business School, for her invaluable guidance, support, and expertise throughout all stages of my thesis. Her dedication, patience, and insightful feedback have been instrumental in shaping the direction and quality of my research. I am profoundly grateful for her unwavering commitment to my academic growth and development.  \nI am also immensely grateful to Lars Arne Skår, my supervisor from SAS, for his exceptional support and mentorship during my thesis. His extensive knowledge, technical expertise, and willingness to help always have been invaluable. His guidance helped me navigate the complexities of my research and broadened my understanding of the subject matter. I am indebted to him for his continuous encouragement and valuable input.  \nFurthermore, I thank Vegard Hansen from SAS for his invaluable assistance in facilitating collabora","cbCaihC42HmesG0k","https://ap.wps.com/l/cbCaihC42HmesG0k","pdf",1109341,3,1,70,"English","en",105,"# Abstract\n# Acknowledgements\n# List of Abbreviations & Definitions\n## Common acronyms and metrics\n## Algorithms and platforms","[{\"question\":\"What problems does AutoML target in machine learning workflows?\",\"answer\":\"AutoML targets algorithm selection, hyperparameter optimisation, feature engineering, scalability, and interpretability issues in ML tasks.\"},{\"question\":\"How does this thesis evaluate existing AutoML methods?\",\"answer\":\"It reviews and evaluates state-of-the-art frameworks and techniques, comparing model performance, execution time, and interpretability while analysing strengths, limitations, and applicability across problem domains.\"},{\"question\":\"What is AutoFlex and how does it improve AutoML?\",\"answer\":\"AutoFlex combines interpretable algorithms with automated pre-processing techniques, using both model components and data transformation steps (e.g., scalers and encoding) to enhance input quality and interpretability.\"}]","OPTIMISING MACHINE LEARNING TECHNIQUES - AUTOFLEX: AN AUTOML APPROACH - Master Thesis | PDF",1785943137,176,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"optimising-machine-learning-techniques-autoflex-an-automl-approach-master-thesis","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/optimising-machine-learning-techniques-autoflex-an-automl-approach-master-thesis/127943/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problems does AutoML target in machine learning workflows?","Question",{"text":76,"@type":77},"AutoML targets algorithm selection, hyperparameter optimisation, feature engineering, scalability, and interpretability issues in ML tasks.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does this thesis evaluate existing AutoML methods?",{"text":81,"@type":77},"It reviews and evaluates state-of-the-art frameworks and techniques, comparing model performance, execution time, and interpretability while analysing strengths, limitations, and applicability across problem domains.",{"name":83,"@type":74,"acceptedAnswer":84},"What is AutoFlex and how does it improve AutoML?",{"text":85,"@type":77},"AutoFlex combines interpretable algorithms with automated pre-processing techniques, using both model components and data transformation steps (e.g., scalers and encoding) to enhance input quality and interpretability.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,105,110,115,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":22,"slug":104},"Exam","exam",{"id":106,"doc_module":4,"doc_module_name":47,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":47,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]