Table 4. Predictive model performance assessment comparison

Model	Detailed model	Algorithms	AUC	CA	F1	Precision	Recall	LogLoss
Augmented data evaluation criteria separated model	Type of landscape	kNN	0.972	0.919	0.917	0.922	0.919	0.176
		SVM	0.999	0.971	0.971	0.972	0.971	0.055
		Random Forest	0.969	0.920	0.918	0.925	0.920	0.249
		Neural Network	0.999	0.986	0.986	0.986	0.986	0.033
		Logistics Regression	0.998	0.977	0.977	0.977	0.977	0.060
	Viewing of distance	kNN	0.957	0.862	0.861	0.861	0.862	0.419
		SVM	0.997	0.963	0.963	0.963	0.963	0.110
		Random Forest	0.965	0.880	0.876	0.880	0.880	0.372
		Neural Network	0.995	0.973	0.973	0.973	0.973	0.100
		Logistics Regression	0.995	0.965	0.965	0.965	0.965	0.097
	Preference of landscape	kNN	0.787	0.698	0.695	0.698	0.698	0.609
		SVM	0.953	0.875	0.875	0.875	0.875	0.297
		Random Forest	0.836	0.763	0.763	0.764	0.763	0.521
		Neural Network	0.990	0.952	0.952	0.952	0.952	0.126
		Logistics Regression	0.970	0.915	0.915	0.915	0.915	0.226