In order to compare the accuracy of the aforementioned SMs, four metrics, namely, RMSE, AAE, MAE, and coefficient-of-determination ($R2)$ were used. The value of $R2$ for MLR, PR-2, PR-3, PR-4, GPR, NN, RVR, and SVR were found to be 0.979, 0.996, 0.998, 0.992, 0.991, 0.982, 0.978, and 0.981, respectively. Generally, a model with the value of $R2$ closer to 1 depicts high level of prediction accuracy. Based on the aforementioned premise, it is inferred that PR, is the most accurate SM out of the six contestants, while GPR is the second most accurate SM. It must be mentioned here that, although PR outperforms GPR on accuracy, unlike PR, GPR has the capability of calculating uncertainty related to the predicted values of the SIF, due to which, GPR can be adaptively trained to further increase its accuracy. As a result, GPR has been chosen as the best SM (out of six competing SMs) to predict the SIF of a propagating crack, the details of which are given in Ref. [41].