Which property is used to specify the method for tree selection based on validation data?

The property used to specify the method for tree selection based on validation data is the Assessment Measure. This measure serves as a criterion to evaluate the performance of the decision tree model on validation datasets. By defining an assessment measure, practitioners can determine how well the model generalizes to unseen data, which is crucial for avoiding overfitting.

Common assessment measures include metrics like accuracy, root mean square error (RMSE), or others depending on the specific task (regression or classification). In the context of tree selection, the chosen assessment measure guides the algorithm in determining which tree structure provides the best predictive performance based on the validation data, leading to an optimal balance between bias and variance in model building.

While Average Square Error is a specific type of assessment measure particularly for regression tasks, it does not represent the general approach for tree selection based on validation data across various contexts. Regression refers to a type of analysis rather than a specific property for model evaluation. The Gini Index is significant in classification trees to measure impurity but does not function as a means to assess overall tree selection methods based on validation performance. Thus, the broader category of Assessment Measure is essential for guiding model selection effectively.