24. Visualizing Your Tree

Visualizing a Decision Tree

Once we have created a decision tree using sklearn, we can easily visualize it by exporting the tree in Graphviz format, using Graphviz open source graph visualization software.

export_graphviz

Use sklearn.tree.export_graphviz() to export the tree into DOT format. DOT is GraphViz's text file format. It includes human-readable syntax that describes the appearance of the tree graph, including the content of subtrees and the appearance of nodes (i.e. color, width, label).

So for example, assume model is an instance of DecisionTreeClassifier() , and you've already called model.fit() . Then export to DOT format as follows: 

dot_data = export_graphviz(model)

There are a lot of options you can specify at this step, which you can explore in the documentation here . In particular, you can save the data to a file, you can specify whether and how to label the nodes, and you can rotate the tree.

graphviz.Source

To render a ready-made DOT source code string, create a Source object holding your DOT string. 

from graphviz import Source
graph = graphviz.Source(dot_data)

Then, display the graph directly in the Jupyter notebook: 

graph

Example

So, for example, if we create the following small dataset, 

rng = np.random.RandomState(0)
X = rng.normal(size=(50, 2))

and set a target variable, y , equal to 0 by default, and equal to 1 if X_1 > 1.2 , 

y = np.zeros(X.shape[0], dtype=np.int)
y[X[:, 1] > 1.2] = 1

and fit a tree to it, 

tree = DecisionTreeClassifier().fit(X, y)

we can run the following code, 

from sklearn.tree import export_graphviz
from graphviz import Source

treedot = export_graphviz(tree, out_file=None)
treegraph = Source(treedot)
treegraph

and we'll then see the following:

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