## 6 Prediction Performance Evaluation by Reducing the Input Dimension of ML Model
Figure 18 displays the prediction performance graph of the NN model based on  
Figure 17: Prediction performance of the NN model based on the selected number of features: (**a**) prediction results of a model with 17 features trained using a dataset of 400 instances; (**b**) prediction results of a model with 11 features trained using a dataset of 200 instances. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature Count** & **Dataset Count** & **RMSE** & **MSE** & **MAE** & \(\mathbf{R^{2}}\) \\ \hline
17 & 400 & 0.12 & 0.01 & 0.09 & 0.94 \\
11 & 200 & 0.12 & 0.02 & 0.09 & 0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of predictive performance indices by feature selection count. the number of input features shown in Figure 7, alongside prediction performance for PLS and PCA depending on the number of newly created components via feature extraction. From the figure, it is evident that employing a feature extraction method instead of a feature selection method for reducing input dimensions yields comparatively better prediction performance, even with fewer input variables. Both feature selection and PCA techniques exhibit a significant drop in R\({}^{2}\) when the number of input variables is limited to two-three, followed by an enhancement in prediction performance as more input variables are incorporated. Conversely, the PLS technique shows consistent improvement in prediction performance as the number of input variables increases, without the initial decline observed in other techniques. Among the three techniques evaluated in Figure 18, the PLS technique emerged as the most effective for input dimensionality reduction. Notably, it achieved an approximate coefficient of determination (R\({}^{2}\)) of 0.9 with just five input variables. Based on these findings, the study adopted the PLS technique for input dimensionality reduction and re-optimized the model's hyperparameters to suit the reduced dimensions. A primary objective of reducing input dimensionality in this study was to investigate whether a machine learning model with excellent prediction performance could be developed using a smaller dataset, thereby minimizing computational time and effort required for dataset creation. To validate this, the seismic response prediction performance of the NN model was evaluated after extracting 5 and 9 components from a total of 17 input features using the PLS technique for dimensionality reduction, as presented in Figure 19 and Table 10. A dataset of 200 samples was used to train this model. It was confirmed that a model extracting 9 components from 17 features using the PLS technique demonstrated similar prediction performance with only 200 datasets compared to a model trained with 400 datasets. Notably, a model utilizing just 5 components, which represents approximately 30% of the original 17 features, achieved comparable prediction performance to an original model with 17 input variables trained with 400 datasets, despite being trained with only 200 datasets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Component** & **Dataset Count** & **RMSE** & **MSE** & **MAE** & **R\({}^{2}\)** \\ \hline
9 & 200 & 0.12 & 0.01 & 0.08 & 0.94 \\
5 & 200 & 0.13 & 0.02 & 0.10 & 0.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of predictive performance indices by number of extracted components. Figure 18: Comparative performance of feature selection and extraction methods. In examining model performance across different input feature sets, we also observed that the evaluation metrics (RMSE, MAE, MSE, and R2) remained consistent and stable. This suggests that, beyond assessing predictive accuracy, the results additionally support the robustness of these metrics under varying input conditions, reinforcing their reliability in ML-based seismic response prediction.