What is the structure of a decision tree?

A decision tree is designed as a flowchart that organizes data into branches according to specific feature values, enabling straightforward decision-making processes. Each internal node of the tree represents a feature or attribute where the data is split based on certain criteria, while each branch symbolizes the outcome or decision based on those features. The terminal nodes, or leaves, indicate the final decisions or classifications reached after traversing the tree’s branches.

This structure is effective for both classification and regression tasks in machine learning, allowing for clear visualization and interpretation of how decisions are made based on the input data. It supports understanding the relationships between different features and their influence on outcomes, making it a valuable tool in data analysis. The flexibility and intuitive nature of a decision tree make it appealing for various applications across fields.