- Training dataset should be class-labeled for learning of decision trees in decision tree induction.
- A decision tree represents rules and it is very a popular tool for classification and prediction.
- Rules are easy to understand and can be directly used in SQL to retrieve the records from the database.
- To recognize and approve the discovered knowledge acquired from decision model is a crucial task.
- There are many algorithms to build decision tree:
- ID3 (Iterative Dichotomiser)
- C4.5 (Successor of ID3)
- CART (Classification and Regression Tree)
- CHAID (Chi-square Automatic Interaction Detector)
- A decision tree classifier has a tree type structure which has leaf-nodes and decision nodes.
- A leaf node is that last node of each branch and indicates the class label or value of a target attribute.
- A decision node is the node of a tree which has leaf node or sub-tree. Some test to be carried on each value of decision node to get the decision of class label or to get next sub-tree.
Decision Tree represents for play tennis
