Conversely, the proportions for Boruta and Lasso were approximately 30% and 25%, respectively. In other words, from a physical chemistry perspective, these data-oriented methods chose parameters that are not necessarily easy to generalize in over 70% of cases, which may have contributed to a decline in the aftermentioned performance in cross-validation. Such data-oriented approaches can only deal with raw numerical data, without considering the meaning of the given explanatory variables. They fail to consider the hierarchical nature of the parameters, an issue directly connected to the "Ugly Duckling" theorem. If data-oriented approaches want to account for hierarchical relationships between parameters, they must prepare a suitable scale database. However, this is often impractical in experimental informatics due to the high data acquisition cost, thus leaving the dilemma unresolved.