A class of relative importance measures based on orthonormality transformation (OTMs), has been found to effectively approximate the General Dominance index (GD). In particular, Johnson's Relative Weight (RW) has been deemed the most successful OTM in the literature. Nevertheless, the theoretical foundation of the OTMs remains unclear. To further understand the OTMs, we provide a generalized framework that breaks down the OTM into two functional steps: orthogonalization and reallocation. To assess the impact of each step on the performance of OTMs, we conduct extensive Monte Carlo simulations under various predictors' correlation structures and response variable distributions. Our findings reveal that Johnson's minimal transformation consistently outperforms other common orthogonalization methods. We also summarize the performance of reallocation methods under four scenarios of predictors' correlation structures in terms of the first principal component and the variance inflation factor (VIF). This analysis provides guidelines for selecting appropriate reallocation methods in different scenarios, illustrated with real-world dataset examples. Our research offers a deeper understanding of OTMs and provides valuable insights for practitioners seeking to accurately measure variable importance in various modeling contexts.

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