[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122705-en":3,"doc-seo-122705-105":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},122705,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","AUTOMATED MACHINE LEARNING SYSTEMS - EVALUATION, EASE OF USE, DATA TRANSFORMATION - A Dissertation","Machine Learning (ML) advances have driven the growth of many machine learning frameworks, but users often struggle to select the right option to build effective ML solutions. Automated Machine Learning (AutoML) addresses this by letting users define goals, provide data, and set budgets such as time search, while automating much of the workflow. Despite these benefits, users must still perform data preprocessing, ranging from simple file selection to complex table merging. The dissertation evaluates AutoML usability and research challenges and proposes standards, evaluation methodology, extendible AutoML frameworks, and an end-to-end preprocessing search focused on contextual feature similarities.","AUTOMATED MACHINE LEARNING SYSTEMS:  \nEVALUATION, EASE OF USE, DATA TRANSFORMATION  \nA Dissertation  \nby  \nDIEGO SERAFIN MARTINEZ GARCIA  \nSubmitted to the Office of Graduate and Professional Studies of Texas A&M University  \nin partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY  \nChair of Committee, Xia Hu  \nCommittee Members, Frank Shipman  \nZhangyang Wang  \nYang Shen  \nHead of Department, Scott Schaefer  \nMay 2022  \nMajor Subject: Computer Science  \nCopyright 2022 Diego Serafin Martinez Garcia  \nABSTRACT  \nMachine Learning (ML) has been rapidly progressing through the years due to its versatility to solve different problems. As a result, many machine learning frameworks have been created that are a collection of different algorithms. However, data scientists often struggle to determine which one to use to develop their ML solutions due to many options. For this reason, many Automated Machine Learning (AutoML) systems have been created to help users create ML solutions easily by defining the problem they want to solve, providing the data, and setting a budget such as time search for a solution.  \nThrough the years, many AutoML systems have been developed, and their applications range from solving simple tasks such as tabular classification to more complex such as object detection. Due to many AutoML systems, it becomes challenging for users to determine which one suits them the best because most of the systems focus on specific tasks and data, and sometimes they overlap on the tasks they can solve. Another issue that users need to be aware of is that although most of the search process is already automated, it is necessary for the user to get involved in data preprocessing in most systems. Such preprocessing can be trivial, from selecting images files to more challenging tasks such as merging multiple database tables.  \nAnother aspect of AutoML systems is the research involved in the development. AutoML research can focus on the way of searching, to some more deep processes such as optimizing how models are run. This research leads to the creation of many AutoML systems every year. Creating an AutoML system is challenging since there are many things to consider, from the design to the implementation, and attempting to use an existing system to test a new hypothesis becomes challenging. The challenge of reusing an existing AutoML system is that most systems were designed towards proposing some research improvement rather than usability. Another problem is that these systems are not maintained, and if they are maintained, it is difficult to use them due to the lack of documentation.  \nEnabling the advance on AutoML is challenging. Firstly, it is necessary to standardize components, so there is a more efficient way to compare different frameworks. There are many AutoML systems every year with new search strategies. However, it becomes challenging to objectively compare them since they could be improving in other areas rather than the ones claimed. Secondly, creating an AutoML system should not be challenging since it can stop many researchers from contributing to the field. Creating a new AutoML system with the sole purpose of testing a new component should not be difficult. Thirdly: we identify that state-of-the-art AutoML systems only focus on model selection and hyperparameter tuning while leaving room for improvement on the data preprocessing. To tackle the challenges above, several contributions are made in the preliminary work, and future work is proposed to conclude the dissertation:  \n• The first contribution of this research dissertation is the development of standards for AutoML, which generalize components that have been used for a while and give them proper definitions.  \n• Second, we propose a better methodology for the evaluation of AutoML systems that provide a better understanding of the capabilities of different systems  \n• To alleviate the burden of human efforts to create single-use AutoML syst","cbCaipc7EZ2RRnEx","https://ap.wps.com/l/cbCaipc7EZ2RRnEx","pdf",1019904,1,92,"English","en",105,"# Abstract\n# Contributions and future directions\n## AutoML standardization\n## Evaluation methodology\n## Extendible AutoML framework\n## Preprocessing search with contextual feature similarities","[{\"question\":\"Why do users have difficulty choosing machine learning frameworks for their ML solutions?\",\"answer\":\"Many frameworks provide different algorithms and capabilities, so users face many options without clear guidance on which best fits their problem and data.\"},{\"question\":\"What key problem remains even when much of AutoML is automated?\",\"answer\":\"Most AutoML systems still require user involvement in data preprocessing, from selecting image files to merging multiple database tables.\"},{\"question\":\"What contributions does the dissertation propose to advance AutoML systems?\",\"answer\":\"It proposes AutoML standards, a better evaluation methodology, an extendible framework to reduce manual work for single-use systems, and an end-to-end preprocessing search framework leveraging contextual feature similarities.\"}]","AUTOMATED MACHINE LEARNING SYSTEMS - 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