Which of the following best describes decision trees?

A decision tree is fundamentally a model that utilizes a tree-like structure to represent a series of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. This structure visually maps out the paths from a decision point to various outcomes based on specific conditions. Each node in the tree represents a test on an attribute, each branch represents the outcome of the test, and each leaf node represents a final outcome or decision. This approach makes it easy to visualize and interpret complex decision-making processes.

In contrast, the other options lack this specific characteristic. A graphical representation of financial decisions may utilize various formats, but it does not inherently imply the structured methodology used in decision trees. A linear model for predicting continuous outcomes refers to regression models that do not have the branching structure of decision trees, focusing instead on establishing a relationship between variables through a straight line. Lastly, methods for unsupervised learning involve clustering or association without labeled outcomes, which is distinct from the supervised learning nature of decision trees that rely on labeled input data to make predictions or classify data points. Hence, option B accurately encapsulates the core essence of what decision trees are.