[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117284-en":3,"doc-seo-117284-105":28,"detail-sidebar-cat-0-en-105":89},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":13,"seo_description":14,"update_tm":26,"read_time":27},117284,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Hyperparameter Tuning in Machine Learning to Predict Student Academic Achievement","Student academic achievement prediction is a key topic in educational data mining, since machine learning performance depends on carefully selected hyperparameters. This study compares four hyperparameter optimization methods—HyperOpt, Random Search, Optuna, and Grid Search—to configure machine learning models for predicting student achievement. The evaluation uses 5-fold cross validation for validation and mean absolute error for performance testing. Experimental results indicate that the grid search approach with cross validation yields the most effective predictive performance for student academic achievement.","Hyperparameter Tuning in Machine Learning to Predicting Student Academic Achievement  \nMuhammad Arifina,1, Soni Adiyonoa,2  \na Sistem Informasi, Fakultas Teknik, Universitas Muria Kudus, Indonesia  \n[1](1 arifin.m@umk.ac.id)[ arifin.m@umk.ac.id](1 arifin.m@umk.ac.id) *; [2](2 soni.adiyono@umk.ac.id)[ soni.adiyono@umk.ac.id](2 soni.adiyono@umk.ac.id)  \n* corresponding author  \nARTICLE INFO  \nArticle history  \nReceived  \nRevised Accepted  \nKeywords  \nHyperparameters Gradient Boosting Tree Grid Search  \nRandom Search Optuna  \nABSTRACT  \nPrediction of student academic achievement is a very important research area; this can be seen from the many researchers who conduct research in this area. To make predictions, a machine learning model is needed. Along with their parameters, the majority of machine learning models have associated hyperparameters. However, knowing the right mix of hyperparameters is essential for robust model performance. A methodical procedure called hyperparameter optimization (HPO) aids in determining the appropriate values for them. In this study we compared four hyperparameters tuning techniques, namely HyperOpt, Random Search, Optuna and Grid Search. The results of the hyperparameters from each of these techniques are then used in machine learning algorithms to predict student academic achievement. Validation uses the 5-fold cross validation method while performance testing uses Mean absolute error. From the experimental results it was found that the hyperparameter technique The best method for predicting student academic achievement in machine learning models is gridsearchcv.  \nThis is an open access article under the CC–BY-SA license.  \n1. Introduction  \nPredicting academic achievementis one of the main areas in educational data mining [1] . To date, there are 13,653 documents documented in Scopus that discuss students' academic performance. Research on student academic achievementwas first conducted in 1954 by Reed M. Merrill in order to evaluate student academic achievementon probation. Articles related to student academic achievementfrom 2002 to 2021 totaled 13,016 articles. The country with the highest number of articles related to the topic of student academic achievementis the United States with 4,098 articles, followed by India with 815 articles, China, Spain, Australia with 700 articles each. Meanwhile, Indonesia is ranked 8th with 470 articles. The development of research with a focus on predicting the academic achievementof students in the last ten years has also experienced a remarkable increase. There were 1027 articles in Scopus from 2012-2021. Based on these data, the prediction of student academic achievementis an important and interesting research area to study.  \nStudent academic achievement prediction models generally use the GPA variable as a target. Classify GPA into classes [2]–[5] . In addition, some researchers use regression models to improve student academic achievement[6], [7]. Regression models such as graddient boosting regression tree (GBRT), random forest and neural networks involve a number of hyperparameters that must be setup before using them [8] .  \nThe goal of GBRT is to enhance the regression achievementof a single model by combining many fitted models. As a result, GBRT uses two algorithms: the decision tree (DT) group's regression tree and gradient boosting, a general metalearning approach used to combine single  \nregression tree models [9] . We will concentrate on this model since it is currently the bestperforming approach for the majority of Kaggle contests [10], [11] and because the achievementis greatly influenced by the selection of the hyperparameters.  \nAlgorithms that use machine learning automatically pick up new information and, as a result, modify their internal parameters in response to new information. These parameters are referred to as\"model parameters\" or just \"parameters\" for short. However, there are some settings that must be made beforehand rather ","cbCaiukyr3tyjVqv","https://ap.wps.com/l/cbCaiukyr3tyjVqv","pdf",504723,1,"English","en",105,"# Introduction\n## Research background and dataset trends\n## Target definition and common modeling approaches\n## Hyperparameters and hyperparameter optimization (HPO)\n## Related work and study novelty\n# Method\n## Hyperparameters\n## Research steps","[{\"question\":\"Why is hyperparameter tuning important for predicting student academic achievement?\",\"answer\":\"Hyperparameters determine the model’s structure and strongly influence predictive performance. Proper tuning improves accuracy and robustness when training prediction models.\"},{\"question\":\"Which hyperparameter tuning techniques are compared in this study?\",\"answer\":\"The study compares HyperOpt, Random Search, Optuna, and Grid Search. Each method produces hyperparameter settings used in machine learning models for prediction.\"},{\"question\":\"How is model performance evaluated?\",\"answer\":\"Validation uses 5-fold cross validation, and performance testing uses mean absolute error.\"}]",1785674989,20,{"code":4,"msg":29,"data":30},"ok",{"site_id":23,"language":22,"slug":31,"title":13,"keywords":32,"description":14,"schema_data":33,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":26},"hyperparameter-tuning-in-machine-learning-to-predict-student-academic-achievement","",{"@graph":34,"@context":83},[35,52,66],{"@type":36,"itemListElement":37},"BreadcrumbList",[38,42,46,49],{"item":39,"name":40,"@type":41,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":43,"name":44,"@type":41,"position":45},"https://docshare.wps.com/document/","Document",2,{"item":47,"name":12,"@type":41,"position":48},"https://docshare.wps.com/document/research-report/",3,{"item":50,"name":13,"@type":41,"position":51},"https://docshare.wps.com/document/hyperparameter-tuning-in-machine-learning-to-predict-student-academic-achievement/117284/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":22,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":39,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"Why is hyperparameter tuning important for predicting student academic achievement?","Question",{"text":73,"@type":74},"Hyperparameters determine the model’s structure and strongly influence predictive performance. 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