Table 1.

Comparison between the levels of accuracy, complexity, and interpretability offered by the machine learning algorithms used herein, namely, polynomial regression (PR), LASSO, random forest (RF), artificial neural network (ANN).

ML algorithms	Coefficient of determination R2		Complexity	Interpretability
	Training set	Test set
PR	0.975	0.970	Low (9)	High
LASSO	0.971	0.966	Low (8)	High
RF	0.991	0.965	High (200)	Intermediate
ANN	0.980	0.975	Intermediate (20)	Low

The level of accuracy is described by the coefficient of determination (R²) for the training and test sets. The complexity is described in parenthesis by the number of non-zero parameters in PR and LASSO, the number of trees in RF, and the product of the number of inputs, neurons, and adjustable parameters per neuron in ANN. The “interpretability” describes the degree to which a human can understand the outcome produced by each model.