[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118800-en":3,"doc-seo-118800-105":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},118800,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Binary Classi􀀂cation with Supervised Machine Learning - A Comparative Analysis","Machine Learning has become an effective approach for modeling and examining social challenges such as poverty, education, and health diseases. This study compares the predictive performance of Support Vector Machines (SVM), Decision Trees (DT), and Logistic Regression (LR) for poverty-status classification using a micro dataset extracted from the National Survey on Household Consumption and Expenditure 2013/2014. Accuracy, precision, Cohen’s Kappa, F1-score, and recall evaluate model outputs. R results show all three methods achieve high accuracy, with decision trees reaching 99.61% versus LR 91.09% and linear-kernel SVM 99.24%.","Appl. Math. Inf. Sci. 17, No. 4, 589-598 (2023) 589  \n\n| Applied Mathematics & Information Sciences An International Journal |  |\n| --- | --- |\n| [http://dx.doi.org/10.18576/amis/170407](http://dx.doi.org/10.18576/amis/170407)\u003Cbr>Binary Classi􀀂cation with Supervised Machine Learning: A Comparative Analysis\u003Cbr>Yassine El aachab 1,∗ , Mohammed Kaicer 1 and Youness Jouilil 2\u003Cbr>1Laboratory of Analysis Geometry and Applications, Department of Mathematics, Faculty of Sciences, Ibn Tofail University Kenitra, Morocco\u003Cbr>2Department of Economics, Faculty of Economics and Social Sciences of Mohammedia, Hassan II University of Casablanca, Morocco\u003Cbr>Received: 14 Mar. 2023, Revised: 08 May 2023, Accepted: 6 Jun. 2023\u003Cbr>Published online: 1 Jul. 2023 |  |\n| Abstract: Over the past decade, Machine Learning has become a practical approach for simulating and examining social issues, notably poverty, education, and health diseases. This study compares the performance of various machine learning methods especially Support Vector Machines (SVM), Decision Trees (DT), and Logistic Regression (LR) in predicting poverty status. For this purpose, the present contribution employs a micro dataset which has been extracted from the National Survey on Household Consumption and Expenditure 2013/2014 . Several evaluation metrics such as accuracy, precision, Cohen’s Kappa statistic, F1-score, and recall are used to evaluate the models’ outputs. The R results indicate that the three algorithms achieved high accuracy scores. Therefore, the decision trees have more improvements in terms of accuracy (99.61%) compared to LR (91.09%) and linear kernel SVM methods ( 99.24%) .\u003Cbr>Keywords: Prediction, Decision Trees, Logistic Regression, Support Vector Machines, Classi􀀂cation, Poverty. |  |\n| 1 Introduction\u003Cbr>Machine Learning, a subset of arti􀀂cial intelligence, has lately gained popularity for its capacity to automatedif􀀂cult procedures and make data-driven predictions. Machine learning algorithms have been used in a variety of industries, including 􀀂nancial services, healthcare, and marketing [1, 2] . The growing amount of data available has been one of the main drivers of the success of ML [3] .\u003Cbr>The purpose of this document is to compare statistical classical and machine learning approaches in terms of their accuracy, interpretability, and performance on different types of data sets. Furthermore, we aim to provide insights into when to use each algorithm based on the characteristics of the data and the requirements of the problem. More speci􀀂cally, we focus on comparing three popular machine learning techniques especially the Support Vector Machines (SVM), Decision Trees (DT), and Logistic Regression (LR) for classi􀀂cation tasks.\u003Cbr>Logistic Regression, introduced by David Cox in 1958, is a widely used algorithm for binary classi􀀂cation [2] . Meanwhile, decision trees are a prominent machine learning approach that is employed for both classi􀀂cation and regression tasks. In addition, the SVM is a decision | support tool that is utilized for both classi􀀂cation and regression problems [4, 5] . The previous algorithms have been used extensively in various applications, and each has its own advantages and limitations.\u003Cbr>Indeed, classi􀀂cation is a critical topic in machine learning, with the goal of predicting the class label of an instance based on its attributes. Logistic regression, decision trees, and SVM are popular algorithms used forclassi􀀂cation tasks.\u003Cbr>In this paper, we will compare these algorithms based on their strengths and weaknesses and provide insights into when each one might be more appropriate for a given classi􀀂cation problem. By examining their underlying principles, mathematical formulations, and implementation details, we aim to provide a comprehensive comparison for the purpose ofclassi􀀂cation [6, 7, 8, 9] .\u003Cbr>The rest of this work is structured as follows. We present, in section 2, the materials and research methods needed to conduct t","cbCaiubpcg4HxLJY","https://ap.wps.com/l/cbCaiubpcg4HxLJY","pdf",444740,1,10,"English","en",105,"# Introduction\n## Materials and Methods\n### Logistic Regression","[{\"question\":\"Which supervised machine learning algorithms are compared in the study?\",\"answer\":\"The study compares Support Vector Machines (SVM), Decision Trees (DT), and Logistic Regression (LR) for binary classification of poverty status.\"},{\"question\":\"What dataset is used for training and evaluation?\",\"answer\":\"A micro dataset extracted from the National Survey on Household Consumption and Expenditure 2013/2014 is used.\"},{\"question\":\"Which evaluation metrics are employed to assess model performance?\",\"answer\":\"Accuracy, precision, Cohen’s Kappa statistic, F1-score, and recall are used to evaluate the models’ outputs.\"}]","Binary Classi􀀂cation with Supervised Machine Learning - A Comparative Analysis | PDF",1785720327,25,{"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},"binary-classification-with-supervised-machine-learning-a-comparative-analysis","",{"@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/binary-classification-with-supervised-machine-learning-a-comparative-analysis/118800/",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},"Which supervised machine learning algorithms are compared in the study?","Question",{"text":75,"@type":76},"The study compares Support Vector Machines (SVM), Decision Trees (DT), and Logistic Regression (LR) for binary classification of poverty status.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset is used for training and evaluation?",{"text":80,"@type":76},"A micro dataset extracted from the National Survey on Household Consumption and Expenditure 2013/2014 is used.",{"name":82,"@type":73,"acceptedAnswer":83},"Which evaluation metrics are employed to assess model performance?",{"text":84,"@type":76},"Accuracy, precision, Cohen’s Kappa statistic, F1-score, and recall are used to evaluate the models’ outputs.","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},[93,97,101,105,110,115,120,123,128,131,134],{"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":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":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]