## 4 Results and Discussion
Tables 2 and 3 demonstrate the performance and computational expenses of the models implemented for predicting the data center ambient temperature and energy demand, respectively, that were retrained with the top ten most important features obtained from each feature selection method. The feature selection methods presented in this paper are RFGFIR, RFFPIR, RFSVFIR, XGBGFIR, XGBPFIR, and XGBSVFIR, as shown above in Tables 2 and 3. RFGFIR is a method that computes feature importance during the model training process, making it computationally fast but error prone. Tables 2 and 3 demonstrate that RFGFIR had a fair speed but lower performance than others. On the other hand, RFFFIR can be used to identify relevant features by using permuted samples out-of-bag to compute feature importance (OOB). To compute feature importance, this method requires a trained model and test samples. It shuffles each feature by chance and quantifies the changes in the model's effectiveness. The feature that has a significant impact on model \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Feature Selection Methods**} & \multicolumn{3}{c}{**Evaluation Metrics and Execution Time When Feature Selection Is Applied**} \\ \cline{2-5}  & **MAE** & **RMSE** & **MAPE** & **Execution Time (sec)** \\ \hline RFGFIR & 0.644 & 0.0512 & 0.033 & 102.011 \\ \hline RFPFIR & 0.499 & 0.392 & 0.022 & 175.515 \\ \hline RFSVFIR & 0.42 & 0.237 & 0.018 & 120.790 \\ \hline XGBGFIR & 0.43 & 0.339 & 0.022 & 118.790 \\ \hline XGBPFIR & 0.443 & 0.348 & 0.025 & 123.364 \\ \hline XGBSVFIR & 0.401 & 0.245 & 0.0035 & 98.039 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results for predicting data center ambient temperature with the top 10 most important features chosen from each feature selection method. Ambient temperature (amb_temp) is the target variable. The best-suited FSS can be determined by observing the results in the table. The FSS method with the lowest error metrics and the shortest execution time is the best-suited feature selection method. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Feature Selection Methods**} & \multicolumn{3}{c}{**Evaluation Metrics and Execution Time When Feature Selection Is Applied**} \\ \cline{2-5}  & **MAE** & **RMSE** & **MAPE** & **Execution Time (sec)** \\ \hline RFGFIR & 14.241 & 22.112 & 0.032 & 115.011 \\ \hline RFPFIR & 3.714 & 8.504 & 0.018 & 163.283 \\ \hline RFSVFIR & 1.368 & 6.657 & 0.005 & 142.830 \\ \hline XGBGFIR & 2.329 & 8.413 & 0.024 & 121.785 \\ \hline XGBPFIR & 0.443 & 0.348 & 0.015 & 128.845 \\ \hline XGBSVFIR & 0.401 & 0.235 & 0.004 & 78.682 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results for predicting data center energy demand with the top ten most important features chosen from each feature selection method. Data center energy demand (dcenergy) is the target variable. The best-suited FSS can be determined by observing the results in the table. The FSS method with the lowest error metrics and the shortest excavation time is the best-suited feature selection method. performance is the most important feature for effectively modeling the target problem. As is shown in Tables 2 and 3, RFPFIR outperformed RFGFIR but had a high computational cost. XGBGFIR and XGBPFIR are two others commonly used, importance-based feature selection methods. Like RFGFIR, XGBGFIR can be computed during the model training process using an importance attribute technique. Its value is computed as the average gain across all tree splits. XGBPFIR, on the other hand, requires a trained model and test data to compute the feature importance by permuting samples. XGBGFIR randomly shuffles each feature and calculates the changes in model performance. In this case, the most important feature is the one with significant impact on model performance. As demonstrated in Tables 2 and 3, XGBPFIR outperformed XGBGFIR, RFGFIR, and RFPFIR in terms of performance at a reasonable speed.