Benchmarking of Machine Learning for Predictive Model for Faculty Selection
DOI:
https://doi.org/10.55164/ecbajournal.v17i1.273896Keywords:
faculty selection, predictive modelling, gradient boosting, higher educationAbstract
This study employed the Gradient Boosted Trees Machines Algorithm and conducted benchmarking of machine learning techniques for predictive modeling in faculty selection among students in Southern Thailand. The dataset included 12,125 students with variables such as High School GPA, blood group, district, province, and parent background. Key factors influencing model performance encompassed academic history, province of residence, and parental attributes. The Gradient Boosted Trees model achieved an impressive accuracy of 85% and precision of 87%, effectively identifying chosen faculties. Precision and recall metrics were 0.594 and 0.460 respectively, with an F1 Score of 0.518, underscoring the model's robustness in predicting student choices. Analysis of the SVM model revealed significant coefficients for features such as "BEFOREGPA" and "BLOODGROUP", influencing predictions positively or negatively. The SVM model achieved an F1 score of 0.33, indicating moderate performance in predicting student choices. The outcomes of the Gradient Boosting model demonstrate its effectiveness in predictive tasks, leveraging an iterative tree-building approach to correct errors systematically. However, careful monitoring of model performance is crucial, particularly when significant errors occur, to mitigate potential issues such as overfitting. In addition, from our analysis, it's evident that students' decisions regarding faculty selection are influenced by a complex interplay of various factors. Among these, province of origin and Grade Point Average (GPA) stand out as pivotal determinants shaping students' educational journeys.
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