[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120742-en":3,"doc-seo-120742-105":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},120742,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Automated Machine Learning - Intelligent Binning Data Preparation and Regularized Regression Classfier","Automated machine learning (AutoML) automates the full pipeline from raw datasets to machine learning model development, reducing manual effort for data scientists and enabling non-experts to complete tasks without deep knowledge of statistical inference and core machine learning concepts. A key limitation is that batch-to-batch data quality can vary, producing poor model performance under distribution shift in numerical predictors. This dissertation introduces intelligent binning to mitigate that issue and evaluates regularized regression classifiers (Ridge, Lasso, Elastic Net) after binning for binary classification.","University of Central Florida  \nSTARS  \nElectronic Theses and Dissertations, 2020-  \n2023  \nAutomated Machine Learning: Intellient Binning Data Preparation and Regularized Regression Classfier  \nJianbin Zhu  \nUniversity of Central Florida  \n Part of the Categorical Data Analysis Commons  \nFind similar works at: [https://stars.library.ucf.edu/etd2020](https://stars.library.ucf.edu/etd2020)  \nUniversity of Central Florida Libraries [http://library.ucf.edu](http://library.ucf.edu)  \nThis Doctoral Dissertation (Open Access) is brought to you for free and open access by STARS. It has been accepted for inclusion in Electronic Theses and Dissertations, 2020-by an authorized administrator of STARS. For more information, please [contact STARS@ucf.edu](contact STARS@ucf.edu).  \nSTARS Citation  \nZhu, Jianbin, \"Automated Machine Learning: Intellient Binning Data Preparation and Regularized Regression Classfier\" (2023) . Electronic Theses and Dissertations, 2020-. 1706.  \n[https://stars.library.ucf.edu/etd2020/1706](https://stars.library.ucf.edu/etd2020/1706)  \nAUTOMATED MACHINE LEARNING: INTELLIGENT BINNING DATA PREPARATION AND REGULARIZED REGRESSION CLASSFIER  \nby  \nJIANBIN ZHU  \nB.E. Nanchang University, 1993  \nM.E. Beijing University of Chemical Techcology,1996  \nPh.D. University of Nebraska-Lincoln, 2011  \nM.S. University of Central Florid, 2013  \nA dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Statistics & Data Science in the College of Science at the University of Central Florida Orlando, Florida  \nSpring Term  \n2023  \nMajor Professors: Chung-Ching Morgan Wang  \nABSTRACT  \nAutomated machine learning (AutoML) has become a new trend which is the process of automating the complete pipeline from the raw dataset to the development of machine learning model. It not only can relief data scientists’ works but also allows non-experts to finish the jobs without solid knowledge and understanding of statistical inference and machine learning.  \nOne limitation of AutoML framework is the data quality differs significantly batch by batch. Consequently, fitted model quality for some batches of data can be very poor due to distribution shift for some numerical predictors. In this dissertation, we develop an intelligent binning to resolve this problem. In addition, various regularized regression classifiers (RRCs) including Ridge, Lasso and Elastic Net regression have been tested to enhance model performance further after binning.  \nWe focus on the binary classification problem and have developed an AutoML framework using Python to handle the entire data preparation process including data partition and intelligent binning. This system has been tested extensively by simulations and real datasets analyses and the results have shown that (1) All the models perform better with intelligent binding for both balanced and imbalance binary classification problem. (2) Regression-based methods are more sensitive than tree-based methods using intelligent binning. RRCs can work better than other tree methods by using intelligent binning technique. (3) Weighted RRC can obtain the best results compared to other methods. (4) Our framework is an effective and reliable tool to conduct AutoML.  \nThis dissertation is dedicated to my family.  \nACKNOWLEDGMENTS  \nI would like to thank my advisor, Dr. Morgan C. Wang, for his support and patience. This research could not have been accomplished without his guidance.  \nThank you to Dr. Liqiang Ni, Dr. Rui Xie and Dr. Bruce Caulkins for their help and being members of the committee.  \nI am very grateful to everyone in the department of Statistics & Data science who helped me during my Ph.D. journey.  \nFinally, I would like to thank my wife, Dr. Minrong Zheng, my daughter, Ms. Mingyin Zhu, and my son, Mr. Samuel Zhu, and my parents in China. Without their unconditional love and support during my whole Ph.D. period, I would not have been able to compl","cbCaimwbdAMQSjBv","https://ap.wps.com/l/cbCaimwbdAMQSjBv","pdf",3000566,1,147,"English","en",105,"# Chapter One: Introduction\n## Automated Machine Learning\n## Data Preparation\n## Data Binning\n## Regularized Regression Classifier\n## Research Objectives\n## Document Structure\n# Chapter Two: Research METHOOLOGIES\n## Intelligent Binning Data Preparation\n## Binning Intervals\n## Binning Encoding\n## Binning Size Optimization\n## Regularized Regression Classifier","[{\"question\":\"What problem does this dissertation address in AutoML pipelines?\",\"answer\":\"It addresses the batch-dependent data quality limitation that leads to poor fitted model quality when numerical predictors experience distribution shift.\"},{\"question\":\"How does intelligent binning help model performance?\",\"answer\":\"The dissertation develops intelligent binning to resolve quality and distribution shift issues, and experiments show improved performance for both balanced and imbalanced binary classification.\"},{\"question\":\"Which modeling approach is reported to perform best after binning?\",\"answer\":\"Weighted regularized regression classifiers (RRCs) achieve the best overall results compared with other tested methods.\"}]","Automated Machine Learning - 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