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A Decision Tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. Decision trees are commonly used in  operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal.

Another use of decision trees is as a descriptive means for calculating conditional probabilities. When the decisions or consequences are modeled by computational verb, then we call the decision tree a computational verb decision  tree.

In decision analysis, a “decision tree” — and the closely-related influence diagram — is used as a visual and analytical decision support tool, where the expected values (or expected utility) of competing alternatives are calculated.

Decision trees have traditionally been created manually, as the following example shows:

A decision Tree consists of 3 types of nodes:-

1. Decision nodes – commonly represented by squares

2. Chance nodes – represented by circles

3. End nodes – represented by triangles

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