## 4 Results and Discussion  
In this study, R\({}^{2}\), NSE coefficient, RMSE, and RMSE to standard deviation of measured data are chosen as the criterion for defining the model's output. The database is split into a training data set and a testing data set to evaluate the performance of the presented models. To make a fair comparison, all the models are developed by applying them to the same RFM shear strength training and testing data sets. Figure 3, displays the scatter plot of the actual and the predicted RFM shear strength for the training phase. The analysis of the R\({}^{2}\) together with NSE, RMSE, and RSR for the RFM shear strength data set demonstrates that the SVM achieved a better prediction performance with (R\({}^{2}\) = 0.9655, NSE = 0.9639, RMSE = 0.1135, and RSR = 0.1899) succeeded by the RF model with (R\({}^{2}\) = 0.9545, NSE = 0.9542, RMSE = 0.1279, and RSR = 0.2140), the AdaBoost model with (R\({}^{2}\) = 0.9390, NSE = 0.9388, RMSE = 0.1478, and RSR = 0.2474), and the KNN with (R\({}^{2}\) = 0.6233, NSE = 0.6180, RMSE = 0.3693, and RSR = 0.6181). Figure 4, presenting the predicted RFM shear strength, is plotted with the actual RFM shear strength data. According to the test data set, all models demonstrated very good predictive potential (R\({}^{2}\) > 0.8) with the exception of KNN, which displayed slightly worse results (i.e., R\({}^{2}\) = 0.6304) for the test data set. The result of R\({}^{2}\) demonstrated that all SVM, RF, and AdaBoost models except KNN are appropriate, but the SVM model performed better because it had the highest R\({}^{2}\) value (0.9656), and after that, the RF (0.9181) and AdaBoost (0.8951) models. In comparison to the other models, the KNN model presented the worst estimates with maximum dispersion (Figure 4). Figure 2: The flowchart of the methodology. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Hyperparameter** & **Explanation** & **Optimal Value** \\ \hline \multirow{6}{*}{KNN} & Number of neighbors & Number of nearest neighbors & 5 \\ \cline{2-4}  & Metric & Distance & Euclidean \\  & & parameter—Euclidean/Manhattan/Chebyshev/Mahalanobis & Euclidean \\ \cline{2-4}  & \multirow{3}{*}{Weight} & Uniform—all points in each neighborhood are weighted & \\  & & equally/distance—closer neighbors of a query point have a & Uniform \\ \cline{1-1}  & & greater influence than the neighbors further away & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont_. In addition, the NSE measure was ranked from highest to lowest predictive strength, following the way: SVM (0.9654) > RF (0.9164) > AdaBoost (0.8835) > KNN (0.6076), which is similar to R\({}^{2}\). With regard to RMSE score, the SVM model also had the maximum predictive ability by having the lowest RMSE (0.0153), succeeded by the models RF (0.0797), AdaBoost (0.0941), and KNN (0.1727). Finally, the reliability of all applied models was divided into four groups based on RSR values: unsatisfactory, satisfactory, good, and very good with ranges of RSR > 0.70, 0.60 \(\leq\) RSR \(\leq\) 0.70, 0.50 \(\leq\) RSR \(\leq\) 0.60, and 0.00 \(\leq\) RSR \(\leq\) 0.50, respectively. The RSR value therefore demonstrates very good results throughout all our established models except the KNN model, whose performance is considered to be satisfactory. Figure 5 depicts the bar graphs comparing the R\({}^{2}\), NSE, RMSE, and RSR for the training and testing data sets of all the models. The R\({}^{2}\) defines the degree of co-linearity between our predicted and actual data. The value of RMSE is more focused on large errors than on small errors. A lower RSR indicates a lower RMSE, indicating the model's better predictive efficiency. The SVM model has high R\({}^{2}\) and NSE while lower RMSE and RSR values, revealing that the SVM model is preferable for predicting the RFM shear strength for the testing data.