Optimized Machine Learning Algorithms for Investigating the Relationship Between Economic Development and Human Capital

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dc.authoridGULERYUZ, DIDEM/0000-0003-4198-9997
dc.authoridOZDEN, ERDEMALP/0000-0001-5019-1675
dc.contributor.authorOzden, Erdemalp
dc.contributor.authorGuleryuz, Didem
dc.date.accessioned2024-10-04T18:52:34Z
dc.date.available2024-10-04T18:52:34Z
dc.date.issued2022
dc.departmentBayburt Üniversitesien_US
dc.description.abstractIn Economic Development, human capital was previously seen as production factors but gradually evolved into endogenous growth theories. Most of the previous studies have examined the relationships between economic development and human capital via econometric models. Since this relationship is usually nonlinear and machine learning (ML) models can resolve it better, this study investigates the relationships by employing ML methods to provide a new perspective. For this purpose, the optimized ML methods, namely Bayesian Tuned Support Vector Machine and Bayesian Tuned Gaussian Process Regression (BT-GPR), were performed to develop the prediction model for economic development. The hyperparameters have been optimized with the Bayes method by using different kernel functions to increase SVM and GPR methods' predictive performance. The Multiple Linear Regression model has been employed to make a comparison as an econometric model. The performance of the models is evaluated using three statistical metrics, namely, the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R-2). The BT-GPR with the exponential kernel model has superior prediction ability with the highest accuracy (R-2: 0.9727, RMSE: 0.4022, MAE: 0.3728 in the testing phase). The study shows that the BT-GPR model increases the accuracy of R-2 6.4%, RMSE 10.7%, and MAE 1% compared with other developed models.en_US
dc.identifier.doi10.1007/s10614-021-10194-7
dc.identifier.endpage373en_US
dc.identifier.issn0927-7099
dc.identifier.issn1572-9974
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85115615742en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage347en_US
dc.identifier.urihttps://doi.org/10.1007/s10614-021-10194-7
dc.identifier.urihttp://hdl.handle.net/20.500.12403/3560
dc.identifier.volume60en_US
dc.identifier.wosWOS:000698871900001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofComputational Economicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEconomic developmenten_US
dc.subjectHuman capitalen_US
dc.subjectProductionen_US
dc.subjectOptimized hyperparametersen_US
dc.subjectSVMen_US
dc.subjectGPRen_US
dc.titleOptimized Machine Learning Algorithms for Investigating the Relationship Between Economic Development and Human Capitalen_US
dc.typeArticleen_US

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