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  2. 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.

  3. 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.

  4. Decision tree - Wikipedia

    en.wikipedia.org/wiki/Decision_tree

    Decision trees are commonly used in operations research and operations management. If, in practice, decisions have to be taken online with no recall under incomplete knowledge, a decision tree should be paralleled by a probability model as a best choice model or online selection model algorithm.

  5. 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 ...

  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. Multiclass classification - Wikipedia

    en.wikipedia.org/wiki/Multiclass_classification

    Decision tree learning is a powerful classification technique. The tree tries to infer a split of the training data based on the values of the available features to produce a good generalization. The algorithm can naturally handle binary or multiclass classification problems. The leaf nodes can refer to any of the K classes concerned.

  8. Knapsack problem - Wikipedia

    en.wikipedia.org/wiki/Knapsack_problem

    The most common problem being solved is the 0-1 knapsack problem, which restricts the number of copies of each kind of item to zero or one. Given a set of items numbered from 1 up to , each with a weight and a value , along with a maximum weight capacity ,

  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 ...