In addition to model accuracy, which assesses the agreement between predicted values and actual values in a test set, it is equally important to evaluate the computational efficiency of ML models. This involves examining the time and resources needed for training and making predictions. In our work, CML models were trained using an Intel(R) Xeon(R) CPU E5-2698 v3 @ 2.30 GHz CPU, and GNN models benefited from the computational acceleration provided by the NVIDIA A100-SXM GPU card. The assessment of time consumption in CML models encompasses training, testing, and the substantial time required for optimizing hyperparameters during the cross-validation process.