Decision tree algorithms
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.
Advantages of decision trees
Decision trees have many advantages. They can be easily understood by both technical and business people and...
