Table 1.

Predictive performance of random forest, regression tree, linear regression, kriging models, and generalized additive model (GAM) for training and testing data sets. The random forest, regression tree, and linear regression model had the same set of 66 variables as inputs. The universal kriging and GAM adjusted for well depths.

	Training Data		Testing Data
Model	MSE	% Variation Explained	MSE	% Variation Explained
Random forest	0.97	76.86	2.39	38.27
OOB	2.52	39.64	-	-
Regression tree	2.58	38.08	2.86	26.10
Linear regression	3.18	23.84	3.03	21.85
Generalized additive model	3.40	18.46	3.20	17.45
Ordinary kriging	-	-	3.17	18.20
Universal kriging	-	-	3.06	20.99

MSE, mean squared error; OOB, Out-of-bag data