Algorithm for building Decision Trees, Basic algorithm for inducing a decision tree.

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 Algorithm for building Decision Trees

  • Decision trees are a popular structure for supervised learning. They are constructed using attributes best able to differentiate the concepts to be learned.
  • A decision tree is built by initially selecting a subset of instances from a training set. This subset is then used by the algorithm to construct a decision tree. The remaining training set instances test the accuracy of the constructed tree.
  • If the decision tree classified the instances correctly, the procedure terminates. If an instance is incorrectly classified, the instance is added to the selected subset of training instances and a new tree is constructed.
  • This process continues until a tree that correctly classifies all non-selected instances are created or the decision tree is built from the entire training set.






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