Table 5.

Estimation metrics results for models’ prediction performance based on scenario (1).

Model	Phase	Index values
		Accuracy	Precision	Recall	F1-score	MCC	HSS
LGBM	Train	0.902	0.877	0.902	0.876	0.446	0.117
	Test	0.890	0.853	0.890	0.868	0.126
	All	0.893	0.821	0.906	0.862	0.133
LGAG	Train	0.913	0.908	0.913	0.882	0.298	0.210
	Test	0.924	0.914	0.924	0.902	0.349
	All	0.916	0.909	0.916	0.888	0.312
LGBE	Train	0.949	0.946	0.949	0.945	0.677	0.623
	Test	0.932	0.925	0.932	0.927	0.521
	All	0.944	0.939	0.944	0.940	0.633
LGGJ	Train	0.926	0.917	0.926	0.916	0.495	0.452
	Test	0.925	0.913	0.925	0.916	0.434
	All	0.926	0.916	0.926	0.916	0.477
LGPO	Train	0.940	0.935	0.940	0.936	0.618	0.570
	Test	0.927	0.919	0.927	0.921	0.479
	All	0.936	0.930	0.936	0.931	0.580
XGBC	Train	0.914	0.904	0.914	0.908	0.445	0.430
	Test	0.913	0.907	0.913	0.909	0.410
	All	0.913	0.905	0.913	0.908	0.435
XGAG	Train	0.923	0.912	0.923	0.908	0.446	0.406
	Test	0.928	0.917	0.928	0.918	0.448
	All	0.924	0.913	0.924	0.911	0.446
XGBE	Train	0.959	0.958	0.959	0.957	0.747	0.698
	Test	0.943	0.938	0.943	0.939	0.604
	All	0.954	0.952	0.954	0.951	0.706
XGGJ	Train	0.937	0.932	0.937	0.928	0.574	0.521
	Test	0.931	0.921	0.931	0.923	0.486
	All	0.935	0.928	0.935	0.926	0.548
XGPO	Train	0.947	0.943	0.947	0.942	0.658	0.620
	Test	0.939	0.933	0.939	0.935	0.571
	All	0.944	0.940	0.945	0.940	0.633