Decision Tree Pattern

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.

• AKA: Decision Tree Abstract Type.
• Context:
  • It can be referenced by (instantiated with) a Decision Tree Structure.
  • It can be an input to a Decision Tree Creation System.
• Examples:
  • a Binary-Splits Decision Tree Model.
  • an nary-Splits Decision Tree Model.
  • a Classification Decision Tree Model.
  • a Regression Decision Tree Model.
  • …
• Counter-Example(s):
  • a Probability Distribution Family.
  • a Neural Network Model.
• See: Propositional Model, Predictor Tree Learning Algorithm.

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References

2009

• http://en.wikipedia.org/wiki/Decision_tree_model
  • The decision tree model is the model of computation in which an algorithm is considered to be basically a decision tree, i.e., a sequence of branching operations based on comparisons of some quantities, the comparisons being assigned a unit computational cost. Several variants of decision tree models may be considered, depending on the complexity of the operations allowed in the computation of a single comparison and the way of branching.
  • Decision trees models are instrumental in establishing lower bounds for computational complexity for certain classes of computational problems and algorithms: the lower bound for worst-case computational complexity is proportional to the largest depth among the decision trees for all possible inputs for a given computational problem.
• PMML TreeModel.
  • http://www.dmg.org/v4-0/TreeModel.html
    • The TreeModel in PMML allows for defining either a classification or prediction structure. Each Node holds a logical predicate expression that defines the rule for choosing the Node or any of the branching Nodes.

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.

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