###### Abstract
Recently, SHAP showed promising results in FSS methods for identifying relevant features by explaining the contributions of each feature toward developing an accurate model. Why SHAP? SHAP is a unique class of additive feature attribution values with consistent, missingness, and accuracy properties that computes feature importance based on the game theory concept. It is a model-agnostic method that can be applied to any machine learning or deep learning model. Hence, the ultimate goal of this paper is to identify relevant features based on their importance, which is computed using SHAP in relation to the specified target variables. We compared SHAP's effectiveness with several widely used feature selection methods. To demonstrate the feature selection analysis, we conducted experiments using operational management data streams obtained from an HPC DC: the ENEA-CRESCO6 cluster with 20,832 cores from Italy. The data streams consist of energy consumption, cooling system, and environment-related operational features. In this paper, we use data center energy demand and ambient temperature target variables to demonstrate the FS process in the context of data center industries, allowing for the effective modeling and optimization of DC operations. The contributions of this paper are: (i) Establishing SHAP-values-assisted feature automation method in the context of a data center with non-linear and complex system interactions and configurations to identify relevant features for effectively modeling and optimizing DC operations. This enables DC operators to better understand their DC operational patterns, allowing them to accurately characterize and optimize their DC operations in a cost-effective way. (ii) Enables accurately characterize and identify the underlying patterns and relationships of features in relation to the target variables, allowing operators to make informed decisions. (iii) Analyzing the effectiveness of various importance-based feature selection methods and selecting the best one that allows for the identification of relevant features in relation to the target variables. (iv) Finally, we identify the best feature selection technique for DC characterizations and operational optimizations that attempts to capture significant features, improve model performance, and reduce computational expense. We also analyze and characterize feature dependency and interactions to bet ter understand how a particular feature affects the modeling of the target variable and DC operations. We compared the proposed method to other commonly used feature selection methods. The top ten features of each feature selection method were then chosen, and the predictive models were retrained to identify the best feature selection method in the context of the data center industries. Hence, the best feature selection method is the one which results in better model performance and reduced computational expenses. The main contribution of this paper is therefore to identify and establish essential feature selection methods in the context of data centers to effectively model and reduce the computational expenses of machine learning models for the optimization and characterization of DC operations. The remaining sections of this paper are structured as follows: Section 2 provides a literature review in relation to feature selection techniques, focusing on the techniques used in the FSS space. Section 3 describes the methodology used in this paper. Section 4 presents the experimental results and discussion, and Section 5 provides conclusions and describes future works of the author.