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  2. C4.5 algorithm - Wikipedia

    en.wikipedia.org/wiki/C4.5_algorithm

    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.

  3. Decision tree learning - Wikipedia

    en.wikipedia.org/wiki/Decision_tree_learning

    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 ...

  4. Decision tree model - Wikipedia

    en.wikipedia.org/wiki/Decision_tree_model

    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.

  5. Chi-square automatic interaction detection - Wikipedia

    en.wikipedia.org/wiki/Chi-square_automatic...

    Luchman, J.N.; CHAIDFOREST: Stata module to conduct random forest ensemble classification based on chi-square automated interaction detection (CHAID) as base learner, Available for free download, or type within Stata: ssc install chaidforest. IBM SPSS Decision Trees grows exhaustive CHAID trees as well as a few other types of trees such as CART.

  6. Category:Decision trees - Wikipedia

    en.wikipedia.org/wiki/Category:Decision_trees

    Upload file; Special pages; ... ID3 algorithm; Incremental decision tree; Information gain (decision tree) Information gain ratio; L. Logistic model tree; R.

  7. Information gain (decision tree) - Wikipedia

    en.wikipedia.org/wiki/Information_gain_(decision...

    The feature with the optimal split i.e., the highest value of information gain at a node of a decision tree is used as the feature for splitting the node. The concept of information gain function falls under the C4.5 algorithm for generating the decision trees and selecting the optimal split for a decision tree node. [1] Some of its advantages ...

  8. Decision tree - Wikipedia

    en.wikipedia.org/wiki/Decision_tree

    The node splitting function used can have an impact on improving the accuracy of the decision tree. For example, using the information-gain function may yield better results than using the phi function. The phi function is known as a measure of “goodness” of a candidate split at a node in the decision tree.

  9. Decision problem - Wikipedia

    en.wikipedia.org/wiki/Decision_problem

    A decision problem which can be solved by an algorithm is called decidable. Decision problems typically appear in mathematical questions of decidability , that is, the question of the existence of an effective method to determine the existence of some object or its membership in a set; some of the most important problems in mathematics are ...