Hunt’s Algorithm

In Hunt’s Algorithm, a decision tree is grown in a recursive fashion by partitioning the training records successively purer subsets. Let Dt be the set of training records that are associated with node t and y = {y1, y2…., yc} be the class labels. The following is a recursive definition of Hunt’s algorithm.

Step-1: If all the records in Dt belong to the same class yt, then t is a leaf node labeled as yt.

Step-2: If Dt contains records that belong to more than one class, an attribute test condition is selected to partition the records into smaller subsets. A child node is created for each outcome of the test condition and the records in Dt are distributed to the children based on the outcomes. The algorithm is then recursively applied to each child node.