How do I find which attributes my tree splits on, when using scikit-learn?

I have been exploring scikit-learn, making decision trees with both entropy and gini splitting criteria, and exploring the differences.

My question, is how can I "open the hood" and find out exactly which attributes the trees are splitting on at each level, along with their associated information values, so I can see where the two criterion make different choices?

So far, I have explored the 9 methods outlined in the documentation. They don't appear to allow access to this information. But surely this information is accessible? I'm envisioning a list or dict that has entries for node and gain.

Directly from the documentation ( http://scikit-learn.org/0.12/modules/tree.html ):

from io import StringIO
out = StringIO()
out = tree.export_graphviz(clf, out_file=out)

StringIO module is no longer supported in Python3, instead import io module.

There is also the tree_ attribute in your decision tree object, which allows the direct access to the whole structure.

And you can simply read it

clf.tree_.children_left #array of left children
clf.tree_.children_right #array of right children
clf.tree_.feature #array of nodes splitting feature
clf.tree_.threshold #array of nodes splitting points
clf.tree_.value #array of nodes values

for more details look at the source code of export method

In general you can use the inspect module

from inspect import getmembers
print( getmembers( clf.tree_ ) )

to get all the object's elements