Visualizing Decision Tree Not Using Graphviz/web

Due to some restriction I cannot use graphviz , webgraphviz.com to visualize decision tree (work network is closed from the other world). Question: Is there some alternative util

Solution 1:

Here is an answer that doesn't use either graphviz or an online converter. As of scikit-learn version 21.0 (roughly May 2019), Decision Trees can now be plotted with matplotlib using scikit-learn’s tree.plot_tree without relying on graphviz.

import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree

X, y = load_iris(return_X_y=True)

# Make an instance of the Model
clf = DecisionTreeClassifier(max_depth = 5)

# Train the model on the data
clf.fit(X, y)

fn=['sepal length (cm)','sepal width (cm)','petal length (cm)','petal width (cm)']
cn=['setosa', 'versicolor', 'virginica']

# Setting dpi = 300 to make image clearer than default
fig, axes = plt.subplots(nrows = 1,ncols = 1,figsize = (4,4), dpi=300)

tree.plot_tree(clf,
           feature_names = fn, 
           class_names=cn,
           filled = True);

fig.savefig('imagename.png')

The image below is what is saved.

The code was adapted from this post.