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A decision tree is a decision support recursive partitioning structure that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements.
The left figure below shows a binary decision tree (the reduction rules are not applied), and a truth table, each representing the function (,,).In the tree on the left, the value of the function can be determined for a given variable assignment by following a path down the graph to a terminal.
The problem of learning an optimal decision tree is known to be NP-complete under several aspects of optimality and even for simple concepts. [34] [35] Consequently, practical decision-tree learning algorithms are based on heuristics such as the greedy algorithm where locally optimal decisions are made at each node. Such algorithms cannot ...
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C4.5 is an algorithm used to generate a decision tree developed by Ross Quinlan. [1] C4.5 is an extension of Quinlan's earlier ID3 algorithm.The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referred to as a statistical classifier.
Decision Tree Model. In computational complexity theory, the decision tree model is the model of computation in which an algorithm can be considered to be a decision tree, i.e. a sequence of queries or tests that are done adaptively, so the outcome of previous tests can influence the tests performed next.
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This template produces one row in a "family tree"-like chart consisting of boxes and connecting lines based loosely on an ASCII art-like syntax.It is meant to be used in conjunction with {{Tree chart/start}} and {{Tree chart/end}}.