Our RF model consists of a collection of decision trees trained using a bootstrapping process, which leverages a bagging technique (i.e., ensemble learning) to reduce the variance in the grain size predictions. A grid search approach is used to explore different combinations of RF model hyper-parameters, such as the number of trees , splitting criterion (absolute error), maximum tree depth (3, 4, 5, 6, 7, 8, 9, unlimited), and the number of leaf nodes (unlimited). The optimal hyperparameters are found to be 128 trees with an unlimited number of leaf nodes, a maximum tree depth of 7, no pruning, and one randomly selected feature at each node splitting.