Decision tree fundamentals: which statements accurately describe the structure and navigation of a decision tree diagram?

Introduction / Context:
Decision trees provide a visual and analytical way to represent choices, chance events, and outcomes. They are used in system design (for logic), analytics (for classification and regression), and managerial decision-making (for alternatives and payoffs). Knowing how to read and construct these diagrams is essential across MIS, data science, and operations research contexts.

Given Data / Assumptions:

• Trees are typically drawn with a root that initiates the flow.
• Branches correspond to alternative decisions or outcomes of conditions.
• Leaves or terminal branches list actions, outcomes, or payoffs.

Concept / Approach:
At the root, the process begins. Decision nodes (often squares in managerial diagrams) represent choices; chance nodes (often circles) represent uncertain outcomes with probabilities; terminal nodes (triangles or endpoints) indicate outcomes/actions with values or utilities. Reading from left to right (or top to bottom), each branch indicates a path determined by the condition or decision encountered. This structure supports both logic specification (if-then branching) and quantitative evaluation (expected value calculations).

Step-by-Step Solution:

Verification / Alternative check:
Standard references on decision analysis and machine learning agree on these structural conventions, though orientation (left-to-right vs top-down) may vary; the semantics remain the same.

Why Other Options Are Wrong:

• Any single statement alone is incomplete; all capture essential aspects.
• None: incorrect because each statement accurately reflects decision tree structure.

Common Pitfalls:
Confusing decision trees (with explicit decisions and outcomes) with pure classification trees; both share branching but differ in annotation and purpose.

Final Answer:
All of the above