Predictive models were programmed using the 'caret' package. Acids and bases were modeled separately and labeled as Models A and B, respectively (see Figure ). The test sets were evaluated using both models. The performance of Models A and B was evaluated using confusion matrices (see Table ), which are widely used to evaluate classification models. The confusion matrices tabulate the number of true positives (TP), false positives (FP), true negatives (TN), and false negative (FN) predictions, along with the sensitivity, specificity, and accuracy of the models.