Hyperparameter optimization was performed to improve the prediction performance of the models. Here, three RF hyperparameters, i.e., n_estimators, min_samples_split, and min_samples_leaf, were optimized, and seven XGB hyperparameters, i.e., n_estimators, max_depth, min_child_weight, learning_rate, Gamma, subsample, and colsample_bytree, were optimized (Table 3). In this study, hyperparameter tuning was performed using the tree-structured Parzen estimator (TPE), which is a Bayesian optimization technique. The TPE searches the hyperparameter set with the largest expected imposition (EI) value sequentially based on the results of the previous iteration as follows.