| Title |
A Comparative Study of Machine Learning Algorithms for Regression in Predicting the Academic Performance of Students in General Mathematics |
|
Authors |
Ontolan, MC. G., Sacayan, R. R. and Tubo, B. F. |
|
Publication date |
2024 |
|
Journal |
The Mindanawan Journal of Mathematics |
| Volume |
6 |
| Issue |
1 |
| Pages |
67-78 |
| Publisher |
Department of Mathematics and Statistics MSU-IIT |
| Abstract |
This study explores the application of predictive modeling techniques in assessing the academic performance of Senior High School students in General Mathematics at Notre Dame of Midsayap College, Cotabato City. Employing three distinct machine learning algorithms, namely, multiple linear regression (MLR), random forest regression (RFR), and support vector regression (SVR), the study aims to predict students'GeneralMathematics grades with some explanatory features like family background, junior andsenior high school characteristics. Evaluation of these algorithms鈥� predictive capabilities isconducted utilizing metrics such as Root Mean Square Error (RMSE), Mean Absolute Error(MAE), and adjustedR2. Results indicate that multiple linear regression model exhibitssuperior predictive performance, yielding lower RMSE and MAE values compared to RFRand SVR models, achieving an accuracy prediction of 97.29%. |
| Index terms / Keywords |
multiple linear regression, random forest regression, support vector regression |
| DOI |
|
Mindanao State
University - Iligan Institute of Technology