# Why are my Decision Tree Leafs not pure?

I'm making a using DecisionTreeClassifier from SKlearn (v0.21.3) with its default settings, using Python. I do not want regularize it in any way, I want it to overfit as much as possible.

When drawing the tree out I saw that some of the leafs were not pure. Is this normal? Was the tree not able to separate the samples?

   ...
model = DecisionTreeClassifier(criterion="entropy")
model = modell.fit(X, y)
...


• The default settings include Gini impurity as the criterion, not entropy. Are you sure you set all other values to default / can you share the code sample? Nov 15 '19 at 6:12
• One reason for the tree not splitting some nodes could be that observations sharing the exact same features have different classes. Do you have such data? Nov 15 '19 at 6:14

With default settings the DecisionTreeClassifier does not have any restrictions in terms of complexity as described in the Scikit Documentation.