Hyperparameter Tuning Modeling for Socioeconomic based Academic Analysis
Keywords:
Academic performance; Socioeconomic analysis; Machine learning; Hyperparameter tuning; Gradient Boosting Machine.Abstract
This study analyzes the impact of hyperparameter tuning in improving the performance of predictive models for academic success based on socioeconomic data. To analyze their predictive capabilities, this research focuses on two ML algorithms, Gradient Boosting Machine (GBM) and Random Forest (RF). Using a UCI Machine Learning Repository dataset, this study implements grid search for hyperparameter tuning, optimizing parameters such as learning speed and number of estimators. Results show that GBM consistently outperforms RF, with higher average accuracy (78.64% vs. 77.45%), lower standard deviation (0.0077 vs. 0.0091), and better stability. Paired t-test results (p-value = 0.0081) confirmed the statistical significance of the superiority of GBM. This research contributes to the field by integrating socioeconomic factors into academic performance prediction models, providing valuable insights for data-driven educational decision-making.
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