[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117556-en":3,"doc-seo-117556-105":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},117556,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Comparative Analysis of Nonlinear Models Developed using Machine Learning Algorithms - Research Paper","Machine learning algorithms are increasingly applied where statistical approaches previously dominated, especially for nonlinear datasets. Artificial neural networks, decision tree methods, and support vector machines can offer flexibility, scalability, and higher predictive accuracy versus traditional linear regression or discriminant analysis. This study compares two machine learning algorithms with an advanced statistical discriminant approach on highly nonlinear educational data, using data meta-features to characterize linearity and evaluating effectiveness for nonlinear predictive modeling.","Comparative Analysis of Nonlinear Models Developed using Machine  \nLearning Algorithms  \nMAJA ROŽMAN 1, ALEN KIŠIĆ2, DIJANA OREŠKI3  \n1Faculty of Economics and Business,  \nUniversity of Maribor,  \nRazlagova ulica 14, 2000 Maribor,  \nSLOVENIA  \n2Vern University,  \nPalmotićeva 82/1, 10 000 Zagreb,  \nCROATIA  \n3Faculty of Organization and Informatics,  \nUniversity of Zagreb,  \nPavlinska 2, Varazdin,  \nCROATIA  \nAbstract: -Machine learning algorithms are increasingly used in a vast spectrum of domains where statistical approaches were previously used. Algorithms such as artificial neural networks, classification, regression trees, or support vector machines provide various advantages over traditional linear regression or discriminant analysis. Advantages such as flexibility, scalability, and improved accuracy in dealing with diverse data types, nonlinear problems, and dimensionality reduction, compared to traditional statistical methods are empirically demonstrated in many previous research papers. In this paper, two machine learning algorithms are compared with one statistical method on highly nonlinear data. Results indicate a high level of effectiveness for machine learning algorithms when dealing with nonlinearity.  \nKey-Words: - Machine learning, decision tree algorithm, artificial neural network, predictive models, data characteristics, nonlinear data, artificial intelligence.  \n5HFHLYHG􀀝 -XO\\ 􀀔􀀛􀀏 􀀕􀀓􀀕􀀖􀀑 5HYLVHG􀀝 $SULO 􀀕􀀔􀀏 􀀕􀀓􀀕􀀗􀀑 $FFHSWHG􀀝 0D\\ 􀀔􀀙􀀏 􀀕􀀓􀀕􀀗􀀑 3XEOLVKHG􀀝 -XQH 􀀕􀀓􀀏 􀀕􀀓􀀕􀀗􀀑  \n1 Introduction  \nThis paper is written under the project SIMON: Intelligent system for automatic selection of the machine learning algorithms in the social sciences. The main objective of the SIMON project is to develop an intelligent system that can automatically recommend machine learning algorithms in the social sciences that work better on a particular data set while taking into consideration the data properties of educational and business datasets. Comparative analysis of various machine learning algorithms across a huge number of datasets is a key component of the project's research. In this paper, the focus is on educational data and two groups of machine learning algorithms, machine learning based on error and machine learning based on information. Those algorithms are compared with an advanced statistical approach of discriminant analysis on the dataset which is characterized by  \nhigh level of nonlinearity. Data properties are measured through meta-features. There are various categories of meta-features. In this research, metafeatures that explain data linearity are employed.  \nArtificial intelligence and machine learning development transformed all aspects of our lives. Education is a field that is continuously adapting to new technologies. Development and application of artificial intelligence and machine learning in education have opened new research paths and possibilities for predicting student performance, mostly in creating personalized learning approaches and understanding the factors contributing to educational success. The combination of powerful machine learning algorithms and huge amounts of data created and stored from learning management systems (LMS) leads to significant scientific achievements. This paper investigates the predictive power of two machine learning algorithms of  \ndifferent approaches to learning and one advanced statistical method for LMS data analysis and the development of student success predictive models which serve as the basis for personalized intelligent systems in education. The research presented here aims to evaluate the effectiveness and application of machine learning algorithms on nonlinear data.  \nThe paper is structured as follows. Section two reviews previous related research papers. Section three provides a description of data and a brief overview of machine learning algorithms. Section four gives insights into research results. Section five concludes the paper and gives guidelines for future ","cbCaihMPmdLEbRFq","https://ap.wps.com/l/cbCaihMPmdLEbRFq","pdf",748056,1,5,"English","en",105,"# Introduction\n## Project SIMON and research aim\n# Theoretical Backgrounds\n## Machine learning algorithms for nonlinear predictive model development\n## Machine learning for predictive model development","[{\"question\":\"What is the main objective of this paper?\",\"answer\":\"To evaluate and compare two machine learning algorithms with an advanced statistical discriminant analysis method on highly nonlinear educational data.\"},{\"question\":\"Which dataset characteristics are used in the study?\",\"answer\":\"Data properties are measured through meta-features, particularly those that explain data linearity.\"},{\"question\":\"Why are interpretability and explainability important here?\",\"answer\":\"Because social science and educational datasets are complex and nonlinear, understanding how models work helps ensure insights can be effectively used.\"}]","Comparative Analysis of Nonlinear Models Developed using Machine Learning Algorithms - 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