Abstract—In this study, we propose a novel approach for analyzing the results of Hyperparameter Optimization (HPO) for Random Forest (RF) models by applying Energy Distance (ED), a metric based on pairwise Euclidean distances. This method provides a quantitative measure of the similarities and differences between the configurations of hyperparameters and their corresponding performance metrics. We use a dataset from a hyperparameter optimization experiment for RF, where we explore the relationship between hyperparameter settings and model accuracy. The results indicate that Energy Distance can offer useful insights into the proximity of different hyperparameter configurations and help identify clusters of similar configurations, which can be useful for model selection and optimization.
 
Keywords—energy distance, hyperparameter optimization, random forest, Euclidean distance, machine learning


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