Visualizing decision tree in scikit-learn

Question

I am trying to design a simple Decision Tree using scikit-learn in Python (I am using Anaconda\'s Ipython Notebook with Python 2.7.3 on Windows OS) and visualize it as follo

Answer 1

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