Visualize Decision Tree

import os
 # On Windows you need to set your path to graphviz (C:\\Program Files (x86)\\Graphviz2.38\\bin\\ for example) 
path_to_graphviz = 'C:\\Program Files (x86)\\Graphviz2.38\\bin\\'
os.environ["PATH"] += os.pathsep + path_to_graphviz

from sklearn.datasets import load_iris
from sklearn import tree
iris = load_iris()
clf = tree.DecisionTreeClassifier(max_depth=3)
clf = clf.fit(iris.data, iris.target)

import graphviz

# Calling `export_graphviz` with `out_file=None` returns GraphViz representation (DOT format) of the decision tree as string.
dot_data = tree.export_graphviz(clf, out_file=None) 

# Convert the source code string to be rendered by graphviz
graph = graphviz.Source(dot_data) 
graph

dot_data = tree.export_graphviz(clf, out_file=None, 
                     feature_names=iris.feature_names,  # shows feature names instead of feature index
                     class_names=iris.target_names,  #  shows Names of each of the target classes in ascending numerical order.
                     filled=True,   # coloring nodes by their class (or value for regression)
                     rounded=True,  # draw node boxes with rounded corners 
                     special_characters=True)  
graph = graphviz.Source(dot_data)  
graph