Search Algorithms for Automated Hyper-Parameter Tuning

TL;DR

This paper evaluates automated hyper-parameter tuning methods, specifically grid search and random search, demonstrating their effectiveness in improving machine learning model accuracy for predicting student success in education.

Contribution

It introduces and empirically tests automated hyper-parameter optimization techniques in educational data analysis, highlighting their superiority over manual tuning.

Findings

Automated tuning improves model accuracy on educational data.

Random search outperforms manual hyper-parameter tuning.

Automated methods are effective for non-expert users in education.

Abstract

Machine learning is a powerful method for modeling in different fields such as education. Its capability to accurately predict students' success makes it an ideal tool for decision-making tasks related to higher education. The accuracy of machine learning models depends on selecting the proper hyper-parameters. However, it is not an easy task because it requires time and expertise to tune the hyper-parameters to fit the machine learning model. In this paper, we examine the effectiveness of automated hyper-parameter tuning techniques to the realm of students' success. Therefore, we develop two automated Hyper-Parameter Optimization methods, namely grid search and random search, to assess and improve a previous study's performance. The experiment results show that applying random search and grid search on machine learning algorithms improves accuracy. We empirically show automated methods' superiority on real-world educational data (MIDFIELD) for tuning HPs of conventional machine learning classifiers. This work emphasizes the effectiveness of automated hyper-parameter optimization while applying machine learning in the education field to aid faculties, directors', or non-expert users' decisions to improve students' success.