For each model, we performed 3 folds of cross-validation to systematically assess the model accuracy. The model with the highest cross validation accuracy (i.e. 73%) was considered as the optimized classification model. This optimized classification model consisted of 1200 tress and minimum number of points in each leaf of 4. To avoid overfitting during the training process, when building the model, we set an out-of-bag cross-validation, where only a randomly selected fraction (i.e. square root of the number of variables) of the variables were fed to individual trees. The combination of out-of-bag cross-validation and leaf purity was utilized to calculate the importance of individual variables on the final model. To select the relevant variables, we divided variance explained by each variable by the largest one and selected those that contributed more than 1% to the model, thus 230 out of 2036 variables.