[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119364-en":3,"doc-seo-119364-105":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":20,"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},119364,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Tree-based Machine Learning in Classifying Reverse Migration - Article","Reverse migration is an urgent challenge driven by economic crises, political turmoil, natural disasters, and the COVID-19 pandemic, affecting both returnees and the urban and rural systems connected to migrant labor. Anticipating reverse migration helps policymakers design effective interventions, yet prior research has rarely used machine learning for this task. This study evaluates three tree-based methods (Decision Tree, Random Forest, Gradient Boosted Trees) against linear-based algorithms (Logistic Regression, Fast Last Margin, Generalized Linear Model), using both accuracy and AUC, and identifies Gradient Boosted Trees as the strongest option for predictive performance.","Mathematical Sciences and Informatics Journal  \nVol. 4, No. 1, May 2023, pp. 49-56  \n[http://www.mijuitmjournal.com](http://www.mijuitmjournal.com) DOI : 10 .24191/mij.v4i1 .22138  \n\n| Tree-based Machine Learning in Classifying Reverse Migration\u003Cbr>Azreen Anuar\u003Cbr>Centre of Graduate Studies, Universiti Teknologi MARA, Perak Branch, Seri Iskandar Campus, Malaysia\u003Cbr>[2020864206@student.uitm.edu. my](2020864206@student.uitm.edu. my)\u003Cbr>Nur Huzeima Mohd Hussain\u003Cbr>Department of Built Environment and Technology, Universiti Teknologi MARA, Perak Branch, Seri Iskandar\u003Cbr>Campus, Malaysia\u003Cbr>[nurhu154@uitm.edu.my](nurhu154@uitm.edu.my)\u003Cbr>Hugh Byrd\u003Cbr>Lincoln School of Architecture, University of Lincoln, Lincoln, United Kingdom\u003Cbr>[hbyrd@lincoln.ac.uk](hbyrd@lincoln.ac.uk) |  |  |\n| --- | --- | --- |\n| Article Info | ABSTRACT |  |\n| Article history:\u003Cbr>Received Feb 16, 2023 Revised Apr 08, 2023 Accepted Apr 26, 2023\u003Cbr>Keywords:\u003Cbr>Tree-based machine learning Linear-based machine learning Reverse migration Classification\u003Cbr>Accuracy\u003Cbr>Area Under the Curve\u003Cbr>Corresponding Author: |  | Reverse migration is an increasingly urgent issue as it is influenced by various factors such as economic crises, political turmoil, natural disasters, and the COVID-19 pandemic. Predicting reverse migration can provide valuable insights for policymakers and stakeholders to design appropriate interventions. However, there is a scarcity of studies that have applied machine learning algorithms to this problem. This paper aims to fill the gap in the literature by discussing the application of machine learning algorithms for predicting reverse migration. The study compares the performance of three types of treebased machine learning (Decision Tree, Random Forest, Gradient Boosted Trees) with linear-based algorithms (Logistic Regression, Fast Last Margin, Generalized Linear Model) . In addition to accuracy, this study also measured the area under the curve (AUC) metric, which has been seldom explored in previous research of reverse migration prediction. The findings revealed that tree-based machine learning algorithms performed slightly better than linear-based algorithms in terms of accuracy of prediction, with an improvement of approximately 1% . Based on the accuracy and AUC results, Gradient Boosted Trees is selected as the best algorithm. The findings of this study suggest that machine learning can provide valuable insights into predicting reverse migration. With the use of appropriate machine learning algorithms, policymakers and stakeholders can make more informed decisions to address the challenges posed by reverse migration. |\n| Nur Huzeima Mohd Hussain\u003Cbr>Department of Built Environment and Technology, Universiti Teknologi MARA, Perak Branch, Seri Iskandar Campus, Malaysia.\u003Cbr>[nurhu154@uitm.edu.my](nurhu154@uitm.edu.my) |  |  |\n\n1. Introduction  \nMachine learning, a subset of artificial intelligence, has gained widespread popularity in recent years due to its ability to analyze vast amounts of data and identify patterns and insights that might be difficult for humans to detect. In healthcare, for example, machine learning algorithms are being used to analyze patient data[1] . In finance, machine learning is being used to detect tax avoidance [2] and financial decision-making[3] . These are just a few examples of how machine learning is being applied across a range of domains, highlighting its potential to revolutionize various industries.  \nMachine learning algorithms can be broadly grouped into two categories: linear-based and tree-based. While linear algorithms have been commonly used in the past, tree-based algorithms have gained significant popularity in recent years due to their ability to handle non-linear relationships and interactions between variables. Despite this, there are limited studies that compare the  \nThis is an open access article under a Creative CommonsAttributionShareAlike4 .0 International License (CC BY-SA 4 .0) .  \nperfo","cbCaisJgHAxQCrxS","https://ap.wps.com/l/cbCaisJgHAxQCrxS","pdf",905265,1,9,"English","en",105,"# Introduction\n## Machine learning background\n## Reverse migration context and need for prediction\n# Article Info\n## Keywords and evaluation metrics","[{\"question\":\"Why is predicting reverse migration important?\",\"answer\":\"Reverse migration can significantly affect the social and economic well-being of returnees and the industries that depend on migrant workers. Accurate prediction supports policymakers in designing interventions to reduce negative impacts.\"},{\"question\":\"Which algorithms are compared in the study?\",\"answer\":\"The paper compares three tree-based machine learning models (Decision Tree, Random Forest, Gradient Boosted Trees) with linear-based algorithms (Logistic Regression, Fast Last Margin, Generalized Linear Model).\"},{\"question\":\"What evaluation metrics are used, and which model performs best?\",\"answer\":\"Performance is assessed using accuracy and the area under the curve (AUC). Results indicate tree-based models perform slightly better, and Gradient Boosted Trees is selected as the best algorithm based on accuracy and AUC.\"}]","Tree-based Machine Learning in Classifying Reverse Migration - Article | PDF",1785723924,23,{"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},"tree-based-machine-learning-in-classifying-reverse-migration-article","",{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/tree-based-machine-learning-in-classifying-reverse-migration-article/119364/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is predicting reverse migration important?","Question",{"text":75,"@type":76},"Reverse migration can significantly affect the social and economic well-being of returnees and the industries that depend on migrant workers. Accurate prediction supports policymakers in designing interventions to reduce negative impacts.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which algorithms are compared in the study?",{"text":80,"@type":76},"The paper compares three tree-based machine learning models (Decision Tree, Random Forest, Gradient Boosted Trees) with linear-based algorithms (Logistic Regression, Fast Last Margin, Generalized Linear Model).",{"name":82,"@type":73,"acceptedAnswer":83},"What evaluation metrics are used, and which model performs best?",{"text":84,"@type":76},"Performance is assessed using accuracy and the area under the curve (AUC). Results indicate tree-based models perform slightly better, and Gradient Boosted Trees is selected as the best algorithm based on accuracy and AUC.","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},[93,97,101,105,110,115,120,123,127,130,134],{"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":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]