Table 5.

Supervised machine learning methods used in this study.

Algorithm	Description
RF (Breiman, 2001)	An ensemble learning method that operates by training decision trees. For predicting a class, the output of the random forest is the class predicted by most trees.
LR (Cramer, 2003)	A statistical model that computes the probability of a sample belonging to some class having the log-odds for the class be a linear combination of one or more independent features.
SVM (Boser et al., 1992)	A supervised learning method that maps training samples to points in space, aiming to maximize the width of the margin that separates the two classes. It is versatile because different Kernel functions can be specified for the decision boundary.