Decision Tree based Classification Approach

• Training dataset should be class-labelled for learning of decision trees in decision tree induction.
• A decision tree represents rules and it is very popular tool for classification and prediction.
• Rules are easy to understand and can be directly used in SQL to retrieve the records from database.
• To recognise and approve the discovered knowledge acquired form decision model is very crucial task.
• There are many algorithms to build decision trees:
  • ID3(Iterative Dichotomiser 3)
  • C4.5(Successor of ID3)
  • CART (Classification and Regression Tree)
  • CHAID (CHi-squared Automatic Interaction Detector)

Decision tree representation:

Decision tree classifier has tree type structure which has leaf nodes and decision nodes.

• leaf node is the last node of each branch and indicates class label or value of target attribute.
• A decision node is the node of tree which has leaf node or sub-tree. Some test to be carried on the each value of decision node to get the decision of class label or to get next sub-tree.