The F1-score combines precision and recall into a single metric, offering a balanced view of the accuracy of a model . The confusion matrix provides a tabular representation of true positive (TP), true negative (TN), false positive (FP), and false negative (FN) predictions . Receiver operating characteristic (ROC) curves illustrate the trade-off between the TP rate and the FP rate at different classification thresholds with the area under the curve (AUC) summarizing the performance of the curve . In regression tasks, the mean squared error (MSE) and root mean squared error (RMSE) quantify the average squared differences between the predicted and actual values, and the mean absolute error (MAE) measures the average absolute differences . Additionally, the coefficient of determination (R 2 ) indicates the proportion of variance in the target variable explained by the model .