What is a decision tree in the context of machine learning?

A decision tree is fundamentally a flowchart-like structure that is instrumental in machine learning for both classification and regression tasks. It comprises nodes that represent decisions or tests on attributes, branches that depict the outcome of these decisions, and leaf nodes that signify final categories or values. This structured approach allows decision trees to model complex relationships in data by breaking them down into simpler decision points, thereby facilitating the understanding of how input features influence the eventual prediction.

In classification tasks, the model divides the data into different classes based on feature values, whereas in regression tasks, it predicts continuous outcomes. This versatility makes decision trees a popular and intuitive method for deriving insights from data, enabling users to visually trace how decisions are made at each step.