The 5-fold cross validation (5-fold CV) is a widely used approach for assessing prediction accuracy and validating machine learning models applied to evaluate their efficacy . In a 5-fold CV, the data are randomly divided into 5 folds or groups, and the model's ability is summarized using the sample of model evaluation scores. Moreover, as mentioned earlier, the dataset was randomly divided into training (80%) and testing (20%) subsets to ensure robust model development. Additionally, descriptors were standardized to bring different units onto a common scale without altering their original units, thus enhancing the model's performance. Combining these techniques can avoid issues such as overfitting and underfitting and obtain a sense of how the model will transfer to a different dataset . This rigorous approach aimed to improve the accuracy of the training data, resulting in more precise predictions. Fig. compares the performance of the three algorithms, SVM, RF, and KNN, in the prediction of NHOC at 20% random test data (Fig. ) and at 5-fold crossvalidation (Fig. ). Detailed results can be found in Tables and in the supplementary materials.