Forward Selection Technique to Choose the Best Features in Prediction of Student Academic Performance Based on Naïve Bayes

Abstract

Supervision of academic performance is very important to ensure that students can complete their education on time. There have been many proposed applications of machine learning algorithms to predict students' academic performance. Prediction is done by analyzing a dataset of historical academic of the student's grade. The dataset which analyzed has many variables (features), this can increase complexity and decrease model performance because maybe not all features are relevant. We propose to implement the forward selection algorithm to select features that can improve model performance. The result shows that the performance of predictive models of students academic scores can improve with the application of feature selection.