Decision Tree Pattern

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A Decision Tree Pattern is a hierarchical structure pattern composed of if-then tests (from a decision tree root node through to a decision tree leaf node) which lead to a prediction.



References

2009

2008

  • (Wilson, 2008a) ⇒ Bill Wilson. (2008). “The Machine Learning Dictionary for COMP9414." University of New South Wales, Australia.
    • decision trees: A decision tree is a tree in which each non-leaf node is labelled with an attribute or a question of some sort, and in which the branches at that node correspond to the possible values of the attribute, or answers to the question. For example, if the attribute was shape, then there would be branches below that node for the possible values of shape, say square, round and triangular. Leaf nodes are labelled with a class. Decision trees are used for classifying instances - one starts at the root of the tree, and, taking appropriate branches according to the attribute or question asked about at each branch node, one eventually comes to a leaf node. The label on that leaf node is the class for that instance.

1999

  • (Zaiane, 1999) ⇒ Osmar Zaiane. (1999). “Glossary of Data Mining Terms." University of Alberta, Computing Science CMPUT-690: Principles of Knowledge Discovery in Databases.
    • QUOTE: Decision Tree: A tree-shaped structure that represents a set of decisions. These decisions generate rules for the classification of a dataset. See CART and CHAID.