Consequently, the site preference energy ΔE is most significant for MgLa2Se4 and diminishes with decreasing k64, as the trigonal distortion stabilizes the octahedral site compared to the tetrahedral site. This stabilization occurs through the reduction of both bond length and volume of the octahedral relative to the tetrahedral site, thereby influencing the interplay between bond length and coordination. To probe the determinants of site preference energy ΔE, we examined several parameters, including the ratio of distances (k64), the ionic radii of metal B (rB) and the anion (rX), as well the electronegativity of B-cation (χB) and the anion (χX). Pairwise correlations are shown in a heatmap (Figure ), that reveals significant associations. Noteworthy relationships emerged between ΔE and k64, as well as between ΔE and both rB and χB. Additionally, correlations are apparent between k64 and both rB and χB. These robust linear correlations hint at potential multicollinearity among parameters, which introduces complexity in estimating individual contributions to ΔE, although it aligns with chemical principles. Before proceeding further, we prioritize the evaluation of crucial parameters associated with ΔE through a comprehensive array of feature importance techniques. These methods include fitting a Random Forest model using Recursive Feature Clustering and Gini Importance , as well as employing ANOVA F-value , Mutual Information , Permutation Importance , and LASSO . Consistently across these techniques, factors such as k64 and rB emerge as noteworthy contributors, as highlighted by the LASSO feature importance analysis illustrated in Figure . Given the pairwise correlations between parameters (Figure ) and their importance in terms of feature relevance (Figure ), we directed our attention to addressing multicollinearity to streamline parameter selection for feature engineering. To this end, we evaluated multicollinearity between k64 and rB using different approaches such as variance inflation factor, tolerance values, and variance proportions with a linear regression model . Indeed, strong correlation between k64 and rB is found suggesting the existence of a simple relation between rB and the anion parameter u (correlating with k64).