Student Performance Prediction Using Machine Learning Algorithms

Table 1

Search values/range for hyperparameters.

Algorithm	Hyperparameter	Search values/range
Decision trees	Maximum depth	[3, 5, 7, 10]
	Minimum samples split	[2, 5, 10, 20]
Naïve bayes	Smoothing parameter	[0.1, 0.5, 1.0, 1.5, 2.0]
K-nearest neighbors	Number of neighbors	[3, 5, 10, 20]
	Weight function	[“uniform,” “distance”]
Support vector machine (SVM)	Kernel	[“linear,” “rbf,” “poly”
	Regularization parameter (C)	[0.1, 1, 10, 100]
	Gamma	[0.001, 0.01, 0.1, 1]