## 5 Discussion and Conclusions  
In this study, ten different regression models, namely, the K-NNs, SV, DT, RF, AdaBoost, ETR, GBM, BR, HBGR, and XGBoost methods for predicting student academic achievement are compared, and their performances are improved via hyperparameter optimization. In \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Metrics**MLModels** & **DT** & **RF** & **K-NNs** & **AdaBoost** & **GBM** & **ETR** & **BR** & **SVR** & **HGBR** & **XGBoost** \\ \hline R\({}^{2}\) & 0.89064 & 0.92980 & 0.913353 & 0.928917 & 0.94696 & 0.929619 & 0.143169 & 0.951915 & 0.945142 & 0.947417 \\ \hline MSE & 0.09043 & 0.05804 & 0.071652 & 0.058781 & 0.04386 & 0.058201 & 0.708542 & 0.039763 & 0.045364 & 0.043482 \\ \hline RMSE & 0.30072 & 0.24092 & 0.267678 & 0.242448 & 0.20942 & 0.241248 & 0.841749 & 0.199406 & 0.212988 & 0.208524 \\ \hline MAE & 0.23894 & 0.18702 & 0.211018 & 0.192703 & 0.16455 & 0.188392 & 0.706746 & 0.159441 & 0.167299 & 0.163836 \\ \hline MSE & 0.01554 & 0.01037 & 0.012866 & 0.010225 & 0.00767 & 0.010056 & 0.015648 & 0.007036 & 0.008068 & 0.007365 \\ \hline MedAE & 0.20684 & 0.15611 & 0.184793 & 0.165127 & 0.13061 & 0.151219 & 0.617039 & 0.131272 & 0.135331 & 0.129909 \\ \hline MaxEr & 0.93889 & 0.85434 & 0.127994 & 0.777161 & 0.86834 & 0.842619 & 1.997979 & 0.850702 & 0.872232 & 0.84852 \\ \hline ExVar & 0.89066 & 0.92981 & 0.913982 & 0.929092 & 0.94699 & 0.92962 & 0.143717 & 0.951932 & 0.945184 & 0.947445 \\ \hline MARE & 0.47907 & 0.37021 & 0.418841 & 0.399577 & 0.31120 & 0.353887 & 1.587274 & 0.2846 & 0.329849 & 0.314439 \\ \hline MSRE & 17.7219 & _7.69777_ & 10.62791 & 0.183885 & 0.58244 & 6.593987 & 142.0118 & 4.111488 & 6.763004 & 6.198054 \\ \hline RMSRE & 4.20974 & 2.77449 & 3.260048 & 3.29224 & 2.41918 & 2.56786 & 11.91687 & 0.272681 & 2.600578 & 2.489589 \\ \hline RRMSE & 2.18522 & 1.1402 & 1.692244 & 1.708955 & 1.25756 & 1.332948 & 6.188577 & 1.05254 & 1.349923 & 1.29231 \\ \hline MBE & \(-\)0.0038 & \(-\)0.0022 & \(-\)0.022821 & \(-\)0.01203 & 0.0043 & \(-\)0.00106 & \(-\)0.02129 & 0.003662 & 0.005871 & 0.00478 \\ \hline erMAX & 84.07117 & 45.6306 & 61.077 & 55.02174 & 39.9395 & 42.80196 & 176.9299 & 33.52411 & 41.41628 & 44.59608 \\ \hline SD & 0.300694 & 0.24091 & 0.266705 & 0.242149 & 0.20938 & 0.241246 & 0.84148 & 0.199372 & 0.212907 & 0.208469 \\ \hline t-stat & \(-\)0.27779 & \(-\)0.2007 & \(-\)1.8714 & \(-\)1.08761 & 0.45053 & \(-\)0.0964 & \(-\)0.55385 & 0.402016 & 0.603497 & 0.501833 \\ \hline U\({}_{95}\) & 0.026929 & 0.02157 & 0.023885 & 0.021686 & 0.01875 & 0.021605 & 0.075358 & 0.017885 & 0.019067 & 0.018669 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Metrics of the ML models, with hyperparameters selected by the grid search method. this study, generalization capabilities were evaluated using 10-fold cross-validation in the modeling process, and model performances were analyzed through seventeen statistical metrics, namely, R2, MAE, MSE, RMSE, MSLE, MaxEr, ExVar, MedAE, MARE, MSRE, RMSRE, RRMSE, MBE, eMAX, SD, t-stat, and U95. Firstly, our ML models are evaluated, using the seventeen metrics given above, without hyperparameter optimization. According to this analysis, the SVR model, which combines the highest explanatory power and the lowest error level, successfully explained 95% of the variance in the data set, with R2 = 0.95, MSE = 0.04, RMSE = 0.20, and MAE = 0.16, demonstrating the best performance in terms of both generalization capacity and prediction accuracy. The GBM and HGBR models follow the SVR model, with R2 = 0.94 and MAE = 0.18, respectively. On the other hand, the single-tree-based DT and K-NNs methods lagged behind with significantly higher error rates. Afterwards, our ML models underwent hyperparameter optimization with GA and were evaluated with the seventeen metrics.