Prediction of Students’ Performance Based on the Hybrid IDA-SVR Model

Table 1

Comparison of common machine learning methods.

Model	Advantages	Disadvantages
Logistic regression	1. Simple calculation and fast speed	The performance is poor when faced with the multivariate or nonlinear decision boundary
	2. Avoid overfitting through regularization
Naive Bayes	Perform well on small-scale data	Very sensitive to the expression of input data
Decision tree	1. Able to apply to samples with missing attribute values	Easy to overfit
	2. Strong robustness to outliers
Artificial neural networks	Perform well on nonlinear data	1. Long training time
		2. The computational complexity is proportional to the network complexity
Support vector regression	1. Strong generalization ability	Sensitive to the selection of parameters and kernel function
	2. Can apply to high-dimensional nonlinear data
	3. Low computational complexity