[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119218-en":3,"doc-seo-119218-105":30,"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":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},119218,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Employee Turnover Prediction with Supervised Machine Learning - Master Thesis","A master thesis presenting an employee turnover prediction approach using supervised machine learning. The study formulates the problem and motivates the value of forecasting voluntary turnover, then reviews related background on turnover and prior research limitations. Methodology combines ANOVA, chi-square testing, correlation analysis, decision trees, random forests, gradient boosting decision trees, XGBoost, and k-fold cross validation, supported by confusion-matrix based evaluation. Metrics include accuracy, precision, recall, F1 score, and ROC-AUC, alongside data cleaning, encoding, and preprocessing steps.","WISEflow Europe/Oslo(CEST) 28Jun 2023  \n􀀔  \nHandelsh0ysllolen Bl  \nGRA 19703 Master Thesis Thesis 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  \n202310ll11184IIINOOIIWIIT (Anonymisert)  \nZhizhen Li og Xiaoxiao Tao  \nTermin:  \nVurderingsform:  \n202310  \nNorsk 6-trinns sllala (A-F)  \nlnformasjon fra delta􀂐er  \nTittel •: Employee Tumouer Prediction with Superuised Machine Leaming  \nNaun pli ueileder •: Jan Kudliclla  \nlnneholder besuarelsen Nei konfidensielt  \nmateriale7:  \nKan besuarelsenoffentliggj•res?:  \nJa  \nGruppe  \nljruppenaun: (Anonymisert)  \nljruppenummer: 137  \nAndre medlemmer igruppen:  \n-Master Thesis  \nEmployee Turnover Prediction with Supervised Machine Learning  \nStudents:  \nXiaoxiao Tao, Zhizhen Li  \nSupervisor:  \nJan Kudlicka  \nHand-in Date:  \n27.06.2023  \nProgram:  \nMaster of Science in Business Analytics  \nTable of Contents  \nAcknowledgments .............................................................................. iv  \nList of Abbreviation ............................................................................ v  \n1. Introduction ..................................................................................... 1  \n1.1 Problem Formulation ..................................................................................... 1  \n1.2 Contributions ................................................................................................. 1  \n1.3 Structure of the Thesis ................................................................................... 2  \n2. Background...................................................................................... 5  \n2.1 Introduction to Employee Turnover .............................................................. 5  \n2.2 Impact of Employee Voluntary Turnover...................................................... 6  \n2.3 Benefits of Predicting Employee Turnover ................................................... 7  \n2.4 Machine Learning .......................................................................................... 8  \n2.5 Supervised Learning Models for Employee Turnover .................................. 9  \n2.6 Limitation of Previous Research ................................................................. 10  \n3. Methodology .................................................................................. 13  \n3.1 Analysis of Variance (ANOVA) ................................................................. 13  \n3.1.1 F-statistic Calculation .................................................................................................. 14  \n3.2 Chi-square Test ............................................................................................ 15  \n3.2.1 Chi-square Test Statistic Calculation ........................................................................... 15  \n3.3 Correlation Analysis .................................................................................... 16  \n3.3.1 Pearson’s Correlation Coefficient Calculation ............................................................ 17  \n3.4 Decision Tree (DT) ...................................................................................... 17  \n3.4.1 Gini Impurity ................................................................................................................ 18  \n3.5 Random Forest (RF) .................................................................................... 19  \n3.5.1 Bootstrap Sampling ...................................................................................................... 20  \n3.6 Gradient Boosting Decision Tree (GBDT) .................................................. 21  \n3.6.1 Loss Function ............................................................................................................... 22  \n3.7 Extreme Gradient Boosting (XGBoost) ........................","cbCaikIGCkgBWauZ","https://ap.wps.com/l/cbCaikIGCkgBWauZ","pdf",1478215,1,101,"English","en",105,"# Acknowledgments\n# List of Abbreviation\n# Introduction\n## Problem Formulation\n## Contributions\n## Structure of the Thesis\n# Background\n## Introduction to Employee Turnover\n## Impact of Employee Voluntary Turnover\n## Benefits of Predicting Employee Turnover\n## Machine Learning\n## Supervised Learning Models for Employee Turnover\n## Limitation of Previous Research\n# Methodology\n## Analysis of Variance (ANOVA)\n## Chi-square Test\n## Correlation Analysis\n## Decision Tree (DT)\n## Random Forest (RF)\n## Gradient Boosting Decision Tree (GBDT)\n## Extreme Gradient Boosting (XGBoost)\n## K-fold Cross Validation (KCV)\n## Confusion Matrix\n## Relevant Metrics","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"The thesis focuses on predicting employee turnover using a supervised machine learning framework, starting from problem formulation and motivation for predicting voluntary turnover.\"},{\"question\":\"Which machine learning models are used for turnover prediction?\",\"answer\":\"The methodology covers decision trees, random forests, gradient boosting decision trees, and XGBoost, combined with k-fold cross validation to improve evaluation reliability.\"},{\"question\":\"How are the prediction results evaluated?\",\"answer\":\"Evaluation uses a confusion matrix and standard classification metrics including accuracy, precision, recall, F1 score, and ROC-AUC.\"}]","Employee Turnover Prediction with Supervised Machine Learning - Master Thesis | PDF",1785723152,255,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"employee-turnover-prediction-with-supervised-machine-learning-master-thesis","",{"@graph":36,"@context":86},[37,54,69],{"@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/employee-turnover-prediction-with-supervised-machine-learning-master-thesis/119218/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",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 problem does the thesis address?","Question",{"text":76,"@type":77},"The thesis focuses on predicting employee turnover using a supervised machine learning framework, starting from problem formulation and motivation for predicting voluntary turnover.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning models are used for turnover prediction?",{"text":81,"@type":77},"The methodology covers decision trees, random forests, gradient boosting decision trees, and XGBoost, combined with k-fold cross validation to improve evaluation reliability.",{"name":83,"@type":74,"acceptedAnswer":84},"How are the prediction results evaluated?",{"text":85,"@type":77},"Evaluation uses a confusion matrix and standard classification metrics including accuracy, precision, recall, F1 score, and ROC-AUC.","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":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]