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Decision trees - Definition and Overview |
| Related Words: Accord, Acquittal, Action, Agreement, Alternativity, Animus, Appetite, Arbitration, Arrangement, Award, Backbone, Choice, Command, Commitment, Compromise, Conclusion, Condemnation, Consideration |
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In decision theory (for example risk management), a decision tree is a graph of decisions and their possible consequences, (including resource costs and risks) used to create a plan to reach a goal. Decision trees are constructed in order to help with making decisions.
In machine learning, a decision tree is a predictive model; that is, a mapping of observations about an item to conclusions about the item's target value. Each inner node corresponds to variable; an arc to a child represents a possible value of that variable. A leaf represents the predicted value of target variable given the values of the variables represented by the path from the root.
The machine learning technique for inducing a decision tree from data is called decision tree learning, or (colloquially) decision trees.
Decision tree learning is also a common method used in data mining. Here, a decision tree describes a tree structure wherein leaves represent classifications and branches represent conjunctions of features that lead to those classifications [1]. A decision tree can be learned by splitting the source set into subsets based on an attribute value test [1]. This process is repeated on each derived subset in a recursive manner. The recursion is completed when splitting is either non-feasible, or a singular classification can be applied to each element of the derived subset.
Decision trees are also a descriptive means for calculating conditional probabilities.
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