Table 1.

Summary of Modeling Algorithms Used in This Study

Model	Synonym	Model Category	Tuning Parameters
Machine Learning Algorithms
Linear regression	Linear	Simple model	Alpha, Lambda
k-nearest neighbors	KNN	Simple model	K
Random Forest	RF	Ensemble model	mtry
Bagged Model	Bag	Ensemble model	Nonea
Stochastic Gradient Boosting	Gbm	Ensemble model	n.trees; shrinkage, n.minobsinnode
Support vector machine	SVM	Support vector machine	C
Least-squares SVM	LS-SVM	Support vector machine	Cost, loss
L2-Regularized SVM	L2-SVM	Support vector machine	Cost, loss
Deep Learning Algorithm
Deep neural networks	DNN	Neural networks	Rate, L1, L2

Notes: Alpha, Lambda: the penalty parameters in the elastic regression model. C: A regularization parameter that controls the trade-off between the misclassification and width of the margin. Cost: The cost function in Least-squares or L2-Regularized SVM model. Loss: The Loss function in Least-squares or L2-Regularized SVM model. K: A parameter in KNN model that refers to the number of nearest neighbors. mtry: Number of variables randomly sampled as candidates at each decision tree. n.trees: Number of decision trees in the stochastic gradient boosting model. n.minobsinnode: Minimum number of samples in tree terminal nodes. L1: a loss function to minimize the error which is the sum of all the absolute differences between the measured values and predicted values. L2: a loss function to minimize the error which is the sum of all the squared differences between the measured values and predicted values. Rate: The learning rate in deep learning model that controls how much to change the model in response to the estimated error when model weights are updated for each of the iterations. shrinkage: The learning rate in the stochastic gradient boosting model. ^aNone of the parameters were tuned in this model.