Figure 6.

Development and evaluation of machine learning model. Since machine learning aims to predict new data in supervised learning, the test set is always preserved during when the machine learning model is built in order to guarantee generalizability. Ordinarily, the remaining data are further split into the training set, which is used to build models (calculate weights), and the validation set, which is used to validate the generated models and to tune hyperparameters. This training‐validation process is performed using a cross‐validation or holdout method. Finally, performance of the created model is evaluated using a test set that is not used in the model‐building process. MAE indicates mean absolute error; RMSE, root mean square error.