Finding Split point

Goal at each node is to find a best split point. The value of a split point depends upon how well it separates classes.

• SPRINT uses Gini index
• For a data set s containing patterns from n classes, gini(S) is defined as follows : gini(S)=1-(sum p[j]^2) where p[j] is the relative frequency of class j in S.
• If a split divides S into two subsets S1 and S2 , the index of divided data is given by:
  gini_split(S) = (n1/n)gini(s1) + (n2/n)gini(S2)
• To find best split point for a node, each node's attribute is scanned and the attribute having minimum gini index value is chosen to split the node