Decision trees are considered a good predictive model to start with, and have many advantages. Interpretability, variable selection, variable interaction, and the flexibility to choose the level of complexity for a decision tree all come into play.
Decision trees methods are considered classification methods, so the typical use case for a decision tree is predicting a class or category. However, there are also certain types of decision trees, which are known as regression trees, where the output is a continuous variable. In this way, we can begin development models that are a mix of numeric and categorical variables.
Decision trees are heavily used in marketing and advertising, and in any industry where there is a need to segment customers into different groups. They are also used in healthcare for disease and risk classification.
