Which aspect of decision trees contributes to their ease of interpretation?

Decision trees are known for their simplicity and transparency, making them particularly easy to interpret compared to many other predictive modeling techniques. The graphical nature of their representation allows users to visualize how decisions are made based on different input features. Each branch of the tree represents a decision point based on a feature, and the paths lead to outcomes. This intuitive structure helps stakeholders understand the model's logic without needing advanced statistical knowledge.

In contrast, complex algorithms used in deep learning often involve numerous layers and parameters, making interpretation challenging. The use of probabilities does exist in decision trees, particularly in estimating outcomes, but it is not a primary factor in their interpretability. Lastly, extensive numerical data is not a requisite for understanding decision trees; rather, their strength lies in how clearly they present decision paths, irrespective of the volume of data used to build the model.