What defines a decision tree in data analytics?

A decision tree is best defined as a flowchart that maps out decisions and their possible consequences, including outcomes. This structure allows analysts to visualize the decision-making process in a straightforward manner, where each node represents a decision point and branches represent the possible consequences or choices that can stem from that decision.

By breaking down complex decision-making into a tree-like structure, decision trees effectively show how decisions lead to certain outcomes based on different factors, making them valuable tools in analytics for both classification and regression tasks. Their visual nature helps stakeholders understand the logic behind decisions and the consequences of those decisions, aiding in better data-driven outcomes.

In contrast, the other options refer to different concepts in data analytics—predictive algorithms, regression models, and data clustering techniques—each serving distinct purposes but not encapsulating the essence of what a decision tree represents.