A) RF is an algorithm that involves generating many decision trees and then assembling their outputs . The parameters explored for this algorithm are: the number of trees in the forest (100,500,100) and gini and entropy as functions to measure the quality of a split. Details of the RF setup are summarized in Table in the Supplementary Material. is a linear classification model. In this model, the probabilities describing the possible outcomes of a single trial are modeled using a logistic function. Solver parameters have a major impact on results. Five different solvers included in scikit-learn were used: newton-cg, lbfgs, liblinear, sag, and saga (Table in the Supplementary Material). solves classification problems because of its ability to handle high-dimensional data using a kernel function . In SVM, the kernel function is used to map data into high-dimensional space by finding an optimally separating hyperplane. For this study, four different kernels were used: linear, poly, rbf, and sigmoid (Table in the Supplementary Material).