[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119124-en":3,"doc-seo-119124-105":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},119124,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Extension of an Open Machine Learning Teaching Resource by Classification Model Material","This thesis extends an existing Open Educational Resource (OER) available through a GitHub repository by adding classification model materials. It provides an organized introduction to core machine learning concepts and algorithms and then includes further models together with structured metadata for each object, aligned with the OER contribution guidelines and the CC license. The work uses Python and scikit-learn (and standard libraries), with Jupyter Notebook for code exploration, and applies models to a non-specific dataset. A performance comparison evaluates efficiency using multiple metrics, and the written extension delivers algorithm background and comparison results to support effective learning.","Extension of an open Machine Learning teaching resource by classification model material  \nBachelor’s thesis to obtain the bachelor’s degree  \nBachelor of Science (B.Sc.) in Data and Information Science degree program The Faculty of Information Science and Communication Studies  \nat TH Köln-University of Applied Sciences  \nSubmitted by:  \nSubmitted to:  \nSecond reviewer:  \nJulia Frederike Landsiedel  \nProf. Dr. Konrad Förstner  \nDr. Klaus Lippert  \nCologne , August 28, 2023  \nErklärung  \nIch versichere, die von mir vorgelegte Arbeit selbstständig verfasst zu haben. Alle Stellen, die wörtlich oder sinngemäß aus veröffentlichten oder nicht veröffentlichten Arbeiten anderer oder der Verfasserin/des Verfassers selbst entnommen sind, habe ich als entnommen kenntlich gemacht. Sämtliche Quellen und Hilfsmittel, die ich für die Arbeit benutzt habe, sind angegeben. Die Arbeit hat mit gleichem Inhalt bzw. in wesentlichen Teilen noch keiner anderen Prüfungsbehörde vorgelegen.  \nOrt, Datum Rechtsverbindliche Unterschrift  \nAbstract  \nThis thesis aims to extend an existing Open Educational Resource (OER), which is available as a GitHub repository, and provide an organized introduction to basic machine learning (ML) concepts and algorithms. Further models, followed by structured metadata for each object, will be included while adhering to the contribution guidelines of the OERand following the CC license.  \nThe Machine-Learning-OER-Basics repository intends to provide a wide range of benefits by enabling diverse users to apply and distribute machine learning algorithms. The goal of this digital collection is to fill the existing gap for instructional material on using machine learning in OER as well as make it easier to learn ML concepts effectively. These ML models are developed using the programming language Python and the library scikit-learn, among other standard libraries. Jupyter Notebook will make it straightforward for the user to explore the code. In order to apply the models to various practical scenarios, a non-specific data set is selected.  \nThis work is considered a solution approach in that it includes adding classification models.  \nA performance comparison of the models is conducted. This comparative analysis evaluates the efficiency of each model. The examination includes various metrics for measurement.  \nThis work serves as a written extension, providing comprehensive background information on the algorithms utilized within the repositories and the performance comparison.  \nThe OER collection is accessible via GitHub under the CC-BY-4.0 license: Machine-Learning-OER-Basics  \nKeywords: Machine Learning, Classification, Decision Tree Classifier, Boosting, Ensemble models, Random Forest Classifier, Repository, Open Educational Resources (OER)  \nTable of Contents  \nErklärung ........................................................................................................................ I  \nAbstract.......................................................................................................................... II  \nTable of Contents ........................................................................................................ III  \nAcronyms...................................................................................................................... IV  \nList of Tables ................................................................................................................. V  \nList of Figures .............................................................................................................. VI  \n1 Introduction ........................................................................................................... 1  \n2 Structure of the thesis .......................................................................................... 4  \n3 State of Research .................................................................................................. 5  \n3.1 Algo","cbCaitmQFLMLKBwV","https://ap.wps.com/l/cbCaitmQFLMLKBwV","pdf",1140031,1,55,"English","en",105,"# Abstract\n# Table of Contents\n# Acronyms\n# List of Tables\n# List of Figures\n# 1 Introduction\n# 2 Structure of the thesis\n# 3 State of Research\n## 3.1 Algorithms for Decision Trees\n## 3.2 Implementation of Decision Tree Classifier\n## 3.3 Ensemble Methods\n## 3.3.1 Implementation of Random Forest Classifier\n## 3.3.2 Implementation of Gradient Boosting Classifier\n## 3.4 Open Educational Resources\n# 4 Machine-Learning-OER-Basics repository\n## 4.1 Restructuring of the collection\n## 4.2 Additions for handling the collection\n# 5 Data Set\n# 6 Implementation of Machine Learning Algorithms\n## 6.1 Decision Tree Classifier\n## 6.2 Random Forest Classifier\n## 6.3 Gradient Boosting Classifier","[{\"question\":\"What is the main objective of the thesis?\",\"answer\":\"The thesis aims to extend an existing Open Educational Resource by adding classification model materials and providing a structured introduction to basic machine learning concepts and algorithms.\"},{\"question\":\"Which tools and technologies are used to develop the models?\",\"answer\":\"The models are developed using Python and the scikit-learn library, with Jupyter Notebook supporting straightforward exploration of the code.\"},{\"question\":\"How are the classification models evaluated?\",\"answer\":\"A performance comparison is conducted across models, using various metrics to measure and assess the efficiency of each approach.\"}]","Extension of an Open Machine Learning Teaching Resource by Classification Model 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