Gini criterion in decision tree classifier

decision_boundary
decision_trees
python
gini

#1

Hi,
I am trying to apply to decision trees for classification using following code :
from sklearn import tree clf=tree.DecisionTreeClassifier(criterion = 'entropy') clf.fit(X,Y)
I am aware that “entropy” criterion uses information gain to draw decision boundaries but what method is used when we assign “gini” as the criterion ?
Thanks in advance
Syed Danish


#2

@syed.danish

Gini index- a measure of total variance across the K classes. For this reason, the Gini index is referred to as a measure of node purity | a small value indicates that a node contains predominantly observations from a single class.

Hope this helps!

Regards,
Hinduja