Table 7.

Estimation metrics results for models’ prediction performance based on scenario (3).

Model	Phase	Index values
		Accuracy	Precision	Recall	F1-score	MCC	HSS
LGBM	Train	0.932	0.931	0.932	0.931	0.606	0.610
	Test	0.939	0.940	0.939	0.940	0.621
	All	0.934	0.937	0.934	0.934	0.610
LGAG	Train	0.939	0.933	0.939	0.933	0.599	0.594
	Test	0.948	0.964	0.948	0.944	0.636
	All	0.942	0.936	0.942	0.936	0.609
LGBE	Train	0.980	0.980	0.980	0.980	0.882	0.864
	Test	0.971	0.971	0.971	0.971	0.818
	All	0.977	0.977	0.977	0.977	0.864
LGGJ	Train	0.956	0.953	0.956	0.953	0.723	0.706
	Test	0.956	0.953	0.956	0.953	0.695
	All	0.956	0.953	0.956	0.953	0.715
LGPO	Train	0.967	0.966	0.967	0.966	0.801	0.793
	Test	0.965	0.965	0.965	0.965	0.778
	All	0.966	0.965	0.966	0.966	0.794
XGBC	Train	0.942	0.940	0.942	0.941	0.653	0.643
	Test	0.938	0.941	0.938	0.939	0.625
	All	0.941	0.940	0.941	0.940	0.643
XGAG	Train	0.955	0.955	0.955	0.955	0.741	0.728
	Test	0.949	0.952	0.949	0.950	0.696
	All	0.953	0.954	0.953	0.953	0.728
XGBE	Train	0.992	0.991	0.992	0.991	0.951	0.938
	Test	0.986	0.986	0.986	0.985	0.907
	All	0.990	0.990	0.990	0.990	0.939
XGGJ	Train	0.965	0.963	0.965	0.963	0.787	0.777
	Test	0.963	0.962	0.963	0.963	0.762
	All	0.964	0.963	0.964	0.963	0.779
XGPO	Train	0.980	0.980	0.980	0.980	0.886	0.854
	Test	0.964	0.964	0.964	0.964	0.773
	All	0.975	0.975	0.975	0.975	0.854