Final answer:
Decision trees do not inherently require variable transformations, but single decision tree-based classifiers may lack robustness for complex data sets. Ensemble methods like Random Forests are suggested for increased robustness and thoroughness.
Step-by-step explanation:
In the context of a learning-based approach, decision trees, including Classification and Regression Trees (CART), do not necessarily require variable transformations for their operation. These algorithms can manage varying scales of features and do not assume linearity in data like some other techniques. However, single decision tree-based classifiers may not be as robust as desired when dealing with complex data sets, getting trapped in local extremums and thus potentially producing biased or overfitted models, particularly with smaller datasets or those with complex dimensionality.
It's also noted that decision trees can be 'blind' to rare variants, a limitation not shared by Bayesian Networks (BNs). As an alternative, ensemble classifiers such as Random Forests with double-loop cross-validation are recommended for their robustness. They are less prone to overfitting and can provide a more exhaustive search of the feature space, potentially leading to better-performing models. Therefore, while variable transformations are not a requisite for decision trees, supplementary techniques or alternative ensemble methods might be recommended for analytical thoroughness and model reliability.