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Decision trees are a structured method to model sequential choices as a tree of criteria and alternatives. They make complex decisions explicit, support comparison of probabilities and expected outcomes, and derive clear action rules. Commonly used in product, architecture, and governance decision processes.
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Executable approach: can be applied and produces an outcome.
What organizes, connects, or makes decisions possible.
A decision tree represents decisions as successive branches whose rules lead to a class, prediction, or action.
The approach grew from statistical decision analysis and machine learning to derive interpretable rules from features. Libraries such as scikit-learn make trees trainable and testable.
Start at the root with a question. Each branch tests a feature, each path narrows the cases, and a leaf provides the result. Split data for learning and evaluation so the logic does not memorize training cases.
A node represents a test or decision question.
Branches split cases according to a rule.
A leaf contains a class, value, or action.
Overly deep trees learn noise and generalize poorly.
Decision trees are interpretable and useful for classification and regression. They can be unstable and overfit; constraints, validation, and ensemble methods help.
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