Final answer:
A classification tree is used when the output variable is categorical or discrete, while a regression tree is used when the output variable is continuous.
Step-by-step explanation:
A classification tree and a regression tree are both types of decision trees, but they are used for different purposes. A classification tree is used when the output variable is categorical or discrete, and the goal is to classify data into predefined classes or categories. On the other hand, a regression tree is used when the output variable is continuous, and the goal is to predict a numerical value.
For example, let's say we have a dataset of car characteristics such as engine size, horsepower, and weight, and we want to predict the price of the car. In this case, we would use a regression tree. However, if we want to classify cars into different categories such as small, medium, or large based on their size, we would use a classification tree.