Improve decision tree plotting in Jupyter environment #6261
Description
Recently I used built-in visualizer for DecisionTreeClassifier in Jupyter and can say that
its interface could be better (example is taken from docs):
>>> from IPython.display import Image
>>> dot_data = StringIO()
>>> tree.export_graphviz(clf, out_file=dot_data,
feature_names=iris.feature_names,
class_names=iris.target_names,
filled=True, rounded=True,
special_characters=True)
>>> graph = pydot.graph_from_dot_data(dot_data.getvalue())
>>> Image(graph.create_png())
In addition this does not work on Python 3, since at the time of writing pydot cannot be installed for Python 3.
The ideal solution will be something like
from sklearn import tree
tc = tree.DecisionTreeClassifier()
...
tree.plot(tc) # or even tc.plot()
but in this case tree module should depend on pydot and IPython.display.image modules.
I can fix this issue, but what is the best way to do this?