Table 8. Classification on demographic data set: The average and standard deviation of evaluation metrics over 10 different data splits.

The best results are bold-faced.

Methods	Metrics
	Precision	Recall	F1-score	ROC-AUC
Logistic Regression	0.481 ± 0.019	0.691 ± 0.016	0.567 ± 0.018	0.619 ± 0.088
Gaussian Naive Bayes	0.065 ± 0.012	0.235 ± 0.029	0.100 ± 0.014	0.633 ± 0.061
Support Vector	0.482 ± 0.019	0.694 ± 0.014	0.569 ± 0.018	0.557 ± 0.102
K-Nearest Neighbour	0.543 ± 0.092	0.665 ± 0.051	0.577 ± 0.046	0.586 ± 0.106
Random Forest	0.480 ± 0.021	0.687 ± 0.028	0.565 ± 0.024	0.603 ± 0.103
Gradient Boosting	0.547 ± 0.103	0.701 ± 0.033	0.595 ± 0.050	0.558 ± 0.142
AdaBoost	0.523 ± 0.097	0.694 ± 0.031	0.582 ± 0.048	0.541 ± 0.109
Multi-Layer Perceptron	0.543 ± 0.094	0.684 ± 0.031	0.582 ± 0.031	0.609 ± 0.071