## 4 Results and Discussion  
The SHAP-assisted feature selection method established in this paper has been discussed and compared to several importance-based feature selection methods. Feature importance is computed and scored for all input features with a given machine learning model. A higher score indicates that the specific feature contributes more to the problem's modeling effectiveness and efficiency. We compared the SHAP-values-assisted feature selection method with other commonly used methods to determine its suitability and effectiveness for identifying relevant features to effectively mine and optimize data center operations, allowing data center operators to perform data-driven service reliability and availability improvements. To demonstrate the feature selection process in the context of data center industry, we used two widely used machine learning regression models: the RF and XGB prediction models. We decided to use these methods to identify relevant features in the context of data centers because they are widely used for feature selection processes, they are faster, and, when compared to others, the TreeSHAP approximation method has optimized hyperparameters with RF and XGB. The hyperparameter values of the models and the FSS have a significant impact on the models' behavior and performance. Hence, the hyperparameters for each method were tuned as follows: for the RF-based feature selection methods, the maximum depth of the trees was 5 and the estimator, or forest, in the trees was 200; the rest were left as defaults. For the XGB feature selection methods, the maximum depth was 5, the estimator was 200, and the learning rate was 0.01; the rest was left as defaults. All the feature selection methods were fitted and computed in Python. Initially, the models were trained using 70% of the total data streams, considering all input features. Following that, we applied feature selection methods to the trained model to compute the importance of each feature and rank them in priority order. The most important feature received the highest score and was ranked first. We trained the models after computing and ranking features based on their importance for each feature selection method, starting with the topmost-ranked feature and continuing n times to find the optimal feature subset from each feature selection method. The order of ranked features and the combination of optimal subsets for each feature selection method may differ due to the nature of the machine learning models. To evaluate the feature selection methods, we created a testing set comprising 30% of the total data streams with all input features. We then selected the top ten most important features from each feature selection method and retrained the ML model's performance and learning rate to demonstrate the suitability of the FSS methods. We used three evaluation criteria to determine and compare the suitability of each feature selection method. These were the MAE, RMSE, and MAPE evaluation metrics. Hence, the suitable feature selection method is then the method which will produce the most predictable feature subset with the best model performance and computational expense in the context of a data center industry. The results in Tables 2 and 3 are based on the top ten most important features chosen from each feature selection method. The results demonstrate how to determine which feature selection method is best suited to identifying relevant features with respect to the specified target variables in the context of a data center. Note that we selected the ambient temperature and energy demand of the data center as target variables to explore the feature selection process, allowing us to extract the underlying patterns that enable the maintenance of data center operations. Hence, the results in Tables 2 and 3 pertain to the ambient temperature and energy demand target variables, respectively.