Table 6.

Estimation metrics results for models’ prediction performance based on scenario (2).

Model	Phase	Index values
		Accuracy	Precision	Recall	F1-score	MCC	HSS
LGBM	Train	0.916	0.922	0.917	0.919	0.550	0.561
	Test	0.933	0.936	0.933	0.934	0.594
	All	0.921	0.926	0.921	0.923	0.562
LGAG	Train	0.934	0.936	0.934	0.935	0.632	0.638
	Test	0.942	0.946	0.942	0.943	0.656
	All	0.936	0.939	0.936	0.937	0.639
LGBE	Train	0.966	0.968	0.966	0.967	0.817	0.793
	Test	0.957	0.958	0.957	0.957	0.734
	All	0.963	0.965	0.963	0.964	0.794
LGGJ	Train	0.947	0.947	0.947	0.947	0.694	0.664
	Test	0.932	0.935	0.932	0.933	0.590
	All	0.942	0.943	0.942	0.943	0.664
LGPO	Train	0.961	0.962	0.961	0.961	0.780	0.747
	Test	0.946	0.946	0.946	0.946	0.662
	All	0.957	0.957	0.957	0.957	0.747
XGBC	Train	0.944	0.943	0.944	0.943	0.674	0.642
	Test	0.924	0.933	0.924	0.928	0.573
	All	0.938	0.939	0.938	0.939	0.642
XGAG	Train	0.950	0.949	0.950	0.949	0.709	0.691
	Test	0.938	0.945	0.938	0.941	0.652
	All	0.946	0.948	0.946	0.947	0.691
XGBE	Train	0.981	0.980	0.981	0.980	0.885	0.865
	Test	0.972	0.972	0.973	0.972	0.821
	All	0.978	0.978	0.978	0.978	0.867
XGGJ	Train	0.958	0.957	0.958	0.957	0.756	0.732
	Test	0.944	0.950	0.944	0.946	0.682
	All	0.953	0.955	0.953	0.954	0.733
XGPO	Train	0.966	0.968	0.966	0.967	0.817	0.793
	Test	0.957	0.958	0.957	0.957	0.734
	All	0.963	0.965	0.963	0.964	0.794