Here is one liner for those who are using jupyter and sklearn(18.2+) You don’t even need matplotlib for that. Only requirement is graphviz
pip install graphviz
than run (according to code in question X is a pandas DataFrame)
from graphviz import Source
from sklearn import tree
Source( tree.export_graphviz(dtreg, out_file=None, feature_names=X.columns))
This will display it in SVG format. Code above produces Graphviz’s Source object (source_code – not scary) That would be rendered directly in jupyter.
Some things you are likely to do with it
Display it in jupter:
from IPython.display import SVG
graph = Source( tree.export_graphviz(dtreg, out_file=None, feature_names=X.columns))
SVG(graph.pipe(format="svg"))
Save as png:
graph = Source( tree.export_graphviz(dtreg, out_file=None, feature_names=X.columns))
graph.format="png"
graph.render('dtree_render',view=True)
Get the png image, save it and view it:
graph = Source( tree.export_graphviz(dtreg, out_file=None, feature_names=X.columns))
png_bytes = graph.pipe(format="png")
with open('dtree_pipe.png','wb') as f:
f.write(png_bytes)
from IPython.display import Image
Image(png_bytes)
If you are going to play with that lib here are the links to examples and userguide