In SAS Enterprise Miner's Decision Tree node, which type of target variable can be used?

The Decision Tree node in SAS Enterprise Miner is versatile in handling different types of target variables for model training. The correct answer signifies that it can indeed work with a range of target variable types, including:

• Nominal variables, which can have multiple unique categories. These are categorical variables without a defined order; for instance, types of fruits or different customer segments.

• Interval variables, which represent numeric values with meaningful intervals. This allows for modeling numeric outcomes where the distance between values is significant, such as predicting sales amounts or temperatures.

• Binary variables, which are a specific case of nominal variables, limited to two categories. Examples include yes/no responses or success/failure outcomes.

This flexibility allows users to apply decision trees to a wide variety of prediction tasks across multiple domains and data characteristics, making it a powerful tool for classification and regression analyses. The inclusion of all these target variable types enhances the applicability of the Decision Tree node in diverse analytical scenarios.