How do decision trees function as a machine learning method?

Decision trees operate by recursively breaking down a dataset into smaller subsets based on the values of input features. This process involves creating a flowchart-like structure where each internal node represents a feature (or attribute), each branch denotes a decision rule, and each leaf node indicates an outcome or classification. By splitting the data into branches based on feature values, decision trees can isolate specific patterns or characteristics within the data that contribute to more accurate predictions.

This hierarchical structure allows decision trees to model complex relationships between features and the target variable without requiring linearity; they can handle both continuous and categorical data. As each decision point is made based on feature values, the tree grows, creating segments where similar outcomes are grouped together. This method effectively simplifies the representation of decision-making processes and enables interpretable visualizations of how different input features influence predictions.

Consequently, decision trees are particularly favored for their clarity and ease of understanding, which contrast with methods that may employ complex linear equations or involve random predictions without a coherent decision-making framework.