The same folds used for cross-validation were used for both models. This enabled the first and second model results to be combined during performance evaluation. Specifically, they are combined via out-of-fold (OOF). OOF is the collection of all the predictions of the machine learning model in the validation data for each fold during cross-validation, rearranged in the same order as the original training data. The cross-validation employed in this study was performed by dividing the objective variable into bins and stratifying the k-fold into 5-fold (stratified k-folds). The number of bins was determined using Sturge's law . This validation design balances the difficulty of quantitatively validating data between each fold. This is expected to build models with good generalization performance.