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Decision tree classifiers are decision trees used for classification. As any other classifier, the decision tree classifiers use values of attributes/features of the data to make a class label (discrete) prediction. Structurally, decision tree classifiers are organized like a decision treein which simple conditions on (usually single) attributes label the edge between an intermediate node and its children. Leaves are labeled by class label predictions. A large number of learning methods have been proposed for decision tree classifiers. Most methods have a tree growing and a pruning phase. The tree growing is recursive and consists in selecting an attribute to split on and actual splitting conditions then recurring on the children until the data corresponding to that path is pure or too small in size. The pruning phase eliminates part of the bottom of the tree that learned noise from the data in order to improve the generalization...
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Dobra, A. (2009). Decision Tree Classification. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_554
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DOI: https://doi.org/10.1007/978-0-387-39940-9_554
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