Evaluating Machine Learning Approaches: A Comparative Study of Random Forest and Neural Networks in Grade Classification
Year: 2025
Type: article
Abstract: This study presents a comprehensive comparative analysis of Random Forest and Neural Networks for grade classification using a dataset of 2,392 high school students. The research involves meticulous data collection and pre-processing, including scaling of numerical features and encoding of categorical variables, to optimize model training. Both models were implemented and rigorously evaluated using standard performance metrics, such as accuracy, precision, recall, and F1-score, with particular attention to their ability to correctly classify students into various grade categories. The results indicate that while the Random Forest model achieved a slightly higher baseline accuracy and benefits from enhanced interpretability, the Neural Networks model demonstrated competitive performance following hyperparameter tuning. The findings underscore the trade-offs between model interpretability and the capacity to capture complex nonlinear relationships, offering valuable insights for practitioners seeking to deploy effective classification strategies in educational settings. Future work may explore ensemble approaches and advanced feature engineering techniques to further improve classification performance.
Authors: Subitha Sivakumar, Sivakumar Venkataraman
Institutions: New Era College, Botho University
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W4409408809