Discretization method to optimize logistic regression on classification of student's cognitive domain

The accuracy level of the student determination in a class often has been paid less attention in educational data mining. So, this paper studies how to improve the performance of classification method to reach the higher of level accuracy. Therefore, we optimize logistic regression using equal frequency discretization method. Here, we test the student data by three intervals, four intervals, and five intervals. For logistic regression, we implement two regularization types, namely: lasso, ridge. Furthermore, to evaluate the results, we use the random sampling technique. Additionally, we measure the results by four classifier metrics, namely: F1, precision, accuracy, and recall. The experimental result shows that this method can be applied to optimize the logistic regression. On logistic regression-lasso and logistic regression-ridge, the three intervals achieve the highest of accuracy level. They can improve the accuracy level about 9% - 9.4%, respectively.