###### Abstract
Notably, diffusion creep was highlighted as the mechanism most influenced by external load, although the study did not explore the pre-creep stage's loading rate impact. Subsequent research by [PERSON] et al. [4] and others emphasized the significance of factors such as grain size, applied load-indentation depth, loading rates, and creep holding time in determining material creep response. These factors were integrated into assessment metrics like the Indentation Size Effect (ISE) and the Loading Rate Sensitivity (LRS), as demonstrated in various studies by [PERSON] et al. [5], [PERSON] et al. [6], [PERSON] et al. [7], [PERSON] et al. [2], and [PERSON] et al. [8; 9]. Indentation creep studies on HEAs have predominantly focused on FCC structure HEAs, noting a pronounced sensitivity of creep behavior during the holding stage to loading rate, applied force, penetration depth, and test temperature. These parameters influence the transition between different material flaw mechanisms, such as dislocation glide and climb and self-diffusion, determining the overall creep response [5; 9; 10; 11; 12]. Despite the extensive research on FCC HEAs, studies on BCC systems have been relatively limited. For instance, [PERSON] et al. [9] investigated the creep behavior of HEA films with both FCC and BCC structures, uncovering significant differences in creep behavior between the FCC HEA (CoCrFeNiCuAl0.25) attributed to the distinct mobility of edge and screw dislocations and the activation volume required for dislocation nucleation between the two crystal structures. Analysis of Input Importance on Mechanical Properties Using Various Models Exploration of the intricate relationship between various inputs and their impact on mechanical properties necessitates a multifaceted analytical approach. Our investigation employs four distinct models--ANOVA [13], Random Forest Regression, [14] Partial Dependence Plots [15] from Artificial Neural Networks (ANNs) [16], and correlation analysis [17]--each chosen for its unique ability to interpret complex data. This integrative strategy not only discusses the rationale behind selecting these models and their implementation but also highlights the specific python libraries utilized, thereby providing a comprehensive view of the dataset's intricacies. Utilizing the **scipy.stats**[18] python library, ANOVA acts as a fundamental statistical tool to compare means across different groups, thereby identifying significant differences that suggest the impact of various input variables on mechanical properties. This method was pivotal for highlighting significant predictors, such as loading speed and creep duration, and understanding their variance on mechanical outcomes. The Random Forest Regression analysis was implemented using the python **sklearn.ensemble.RandomForestRegressor**[19] class. Random Forest Regression delves into non-linear relationships and variable interactions. By constructing multiple decision trees and assessing feature importance, this model excels in revealing intricate dependencies within the data, thus identifying inputs that significantly affect mechanical properties. Partial Dependence Plots from Artificial Neural Networks (ANNs) were employed using the python **sklearn.inspection.plot_partial_dependence**[15] library alongside neural networks developed using the **keras** library (or **tensorflow.keras for TensorFlow users**[20]). ANNs offer an advanced approach to modeling complex relationships. Partial Dependence Plots provide detailed visualizations of how specific inputs influence mechanical properties, highlighting the sophisticated analytical capabilities of ANNs in capturing non-linear interactions. Finally, the correlation analysis used the **pandas.DataFrame.corr** method [21] from the **pandas** library. Correlation analysis serves as a preliminary step to assess linear relationships between variables. This method is essential for identifying potential predictors, setting the stage for a more detailed investigation with the more complex models mentioned above.