Table 2 Final R² values for each layer across ML methods.

	Precision	R² - Bayesian	R² - LR	R² - LSBoost	R² - ANN	R² - RF	R² - SVM
Test	0.1	0.6980	0.6944	0.6743	0.6850	0.6937	0.6487
	0.2	0.9951	0.9952	0.9758	0.9862	0.9939	0.9493
	0.3	0.9950	0.9951	0.9872	0.9954	0.9965	0.9446
	0.4	0.9947	0.9951	0.9926	0.9961	0.9983	0.9654
	0.5	0.9948	0.9951	0.9922	0.9963	0.9988	0.9755
	0.6	0.9951	0.9952	0.9928	0.9972	0.9992	0.9709
	0.7	0.9948	0.9950	0.9944	0.9969	0.9995	0.9696
	0.8	0.9952	0.9952	0.9949	0.9971	0.9996	0.9740
	0.9	0.9951	0.9952	0.9955	0.9973	0.9999	0.9756
Train	0.1	0.6981	0.6989	0.6946	0.6949	0.7000	0.6639
	0.2	0.9986	0.9988	0.9957	0.9957	0.99999	0.9828
	0.3	0.9977	0.9984	0.9944	0.9963	0.99999	0.9606
	0.4	0.9974	0.9970	0.9963	0.9973	0.99999	0.9730
	0.5	0.9972	0.9973	0.9962	0.9972	0.99999	0.9782
	0.6	0.9969	0.9970	0.9965	0.9978	0.99999	0.9764
	0.7	0.9979	0.9979	0.9956	0.9978	0.99998	0.9686
	0.8	0.9963	0.9963	0.9957	0.9973	0.99999	0.9757
	0.9	0.9963	0.9963	0.9957	0.9971	0.99998	0.9769
Test/Train (All)	0.1	0.6981	0.6966	0.6844	0.6900	0.6968	0.6562
	0.2	0.9968	0.9969	0.9857	0.9909	0.9970	0.9659
	0.3	0.9963	0.9968	0.9908	0.9958	0.9983	0.9526
	0.4	0.9961	0.9961	0.9945	0.9967	0.9992	0.9692
	0.5	0.9960	0.9962	0.9942	0.9967	0.9994	0.9768
	0.6	0.9960	0.9961	0.9947	0.9975	0.9996	0.9737
	0.7	0.9963	0.9965	0.9950	0.9973	0.9997	0.9691
	0.8	0.9957	0.9957	0.9953	0.9972	0.9998	0.9749
	0.9	0.9957	0.9957	0.9956	0.9972	0.9999	0.9763

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