The Decision tree reference article from the English Wikipedia on 24-Apr-2004 (provided by Fixed Reference: snapshots of Wikipedia from wikipedia.org)

Decision tree

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.

External Sources

[1] T. Menzies, Y. Hu, Data Mining For Very Busy People. IEEE Computer, October 2003, pgs. 18-25.

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