Decision trees are a simple and interpretable way of representing a decision-making process. They are expressive enough to represent a wide range of decision problems, but they may not be expressive enough for more complex decision problems.
The expressiveness of decision trees is limited by their structure. Each internal node in a decision tree corresponds to a binary decision, which partitions the data into two subsets based on the value of a single attribute. This means that decision trees can only represent decision problems that can be decomposed into a sequence of binary decisions.
However, there are techniques for increasing the expressiveness of decision trees. For example, decision trees can be combined with other machine learning models, such as neural networks or support vector machines, to form more complex models. Additionally, decision trees can be extended to handle continuous-valued attributes, missing data, and noisy data.
