What does the Decision Tree Split Search mechanism focus on for categorical inputs?

In the context of the Decision Tree Split Search mechanism, the focus for categorical inputs is primarily on maximizing the separation of target classes when determining splits. The correct approach incorporates the average target value since it helps in assessing whether a proposed split effectively improves the purity of the resulting nodes. By considering the average target value for potential splits, the mechanism evaluates how well each level of the categorical variable helps to discriminate between the different target class outcomes. This results in a more informed decision on where to make splits within the tree, ultimately leading to better model performance.

The other strategies, while they may have their merits, do not align as closely with the methodology of decision trees in terms of effectively utilizing the information provided by categorical variables for predictive purposes. For example, treating all levels equally or only focusing on the most frequent levels can overlook critical distinctions that less common categories may offer. Similarly, re-binning levels with target rates of 0 or 100% without assessing their contribution to the split may lead to inefficient decisions that do not enhance the model's accuracy. Thus, averaging the target values is the most effective and relevant method for evaluating splits in this context.