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  2. Decision tree - Wikipedia

    en.wikipedia.org/wiki/Decision_tree

    Decision trees, influence diagrams, utility functions, and other decision analysis tools and methods are taught to undergraduate students in schools of business, health economics, and public health, and are examples of operations research or management science methods. These tools are also used to predict decisions of householders in normal and ...

  3. Decision tree learning - Wikipedia

    en.wikipedia.org/wiki/Decision_tree_learning

    Decision tree learning is a method commonly used in data mining. [3] The goal is to create a model that predicts the value of a target variable based on several input variables. A decision tree is a simple representation for classifying examples.

  4. 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.

  5. 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.

  6. ID3 algorithm - Wikipedia

    en.wikipedia.org/wiki/ID3_algorithm

    In decision tree learning, ID3 (Iterative Dichotomiser 3) is an algorithm invented by Ross Quinlan [1] used to generate a decision tree from a dataset. ID3 is the precursor to the C4.5 algorithm , and is typically used in the machine learning and natural language processing domains.

  7. Decision tree pruning - Wikipedia

    en.wikipedia.org/wiki/Decision_tree_pruning

    One of the questions that arises in a decision tree algorithm is the optimal size of the final tree. A tree that is too large risks overfitting the training data and poorly generalizing to new samples. A small tree might not capture important structural information about the sample space.

  8. Decision mining - Wikipedia

    en.wikipedia.org/wiki/Decision_mining

    Decision mining is a way of enhancing process models by analyzing the decision points in the model and finding the rules in those decision points based on data attributes. The rules for decision mining is extracted using decision tree algorithms, that analyses decision points to find out which properties of a case might lead to taking certain ...

  9. Decision model - Wikipedia

    en.wikipedia.org/wiki/Decision_model

    For example, for decision analysis, the sole action axiom occurs in the Evaluation stage of a four-step cycle: Formulate, Evaluate, Interpret/Appraise, Refine. Decision models are used both to model a decision being made once, as well as to model a repeatable decision-making approach that will be used over and over again.