Click here to Skip to main content
65,938 articles
CodeProject is changing. Read more.
Articles
(untagged)

ID3 Decision Tree Algorithm in C#

0.00/5 (No votes)
21 Oct 2003 4  
Sample of ID3 Decision Tree Algorithm in C#

Introduction

The algorithm ID3 (Quinlan) uses the method top-down induction of decision trees. Given a set of classified examples a decision tree is induced, biased by the information gain measure, which heuristically leads to small trees. The examples are given in attribute-value representation. The set of possible classes is finite. Only tests, that split the set of instances of the underlying example languages depending on the value of a single attribute are supported.

Details

Depending on whether the attributes are nominal or numerical, the tests either

  • have a successor for each possible attribute value, or
  • split according to a comparison of an attribute value to a constant, or depending on if an attribute value belongs to a certain interval or not.

The algorithm starts with the complete set of examples, a set of possible tests and the root node as the actual node. As long as the examples propagated to a node do not all belong to the same class and there are tests left,

  • a test with highest information gain is chosen,
  • the corresponding set of successors is created for the actual node,
  • each example is propagated to the successor given by the chosen test,
  • ID3 is called recursively for all successors.

How it works

The core of sample is builded with 3 classes (Attribute, TreeNode and DecisionTreeID3).

  • TreeNode - are the nodes of the decision tree;
  • Attribute - is the class with have a name e any possible values;
  • DecisionTreeID3 - is the class what get a data samples and generate a decision tree.

License

This article has no explicit license attached to it but may contain usage terms in the article text or the download files themselves. If in doubt please contact the author via the discussion board below.

A list of licenses authors might use can be found here