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 D_t be the set of training records that are associated with node t and y = {y₁, y₂…., y_c} be the class labels. The following is a recursive definition of Hunt’s algorithm.

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

Step-2: If D_t 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 D_t are distributed to the children based on the outcomes. The algorithm is then recursively applied to each child node.