Visualizing A Decision tree using GraphViz and Pydotplus.

Graphviz is an open-source graph visualization software. Graph visualization is a way of representing structural information as diagrams of abstract graphs and networks.

export_graphviz:

This function generates a GraphViz representation of the decision tree in dot format, which is then written into an output file (‘out_file’).

We’ll be using the iris dataset to visualize the decision tree formation.

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

Creating a dot file.

dot_data = export_graphviz(clf, out_file=None,feature_names=iris.feature_names,filled=True)

out_file

It gives the name of the output file. If None, the result is returned as a string.

feature_names

Names of each of the features in the dataset. If None generic names like feature_0,feature_1,feature_2… will be used.

filled

It takes a boolean value. If True it paints the node to indicate the majority class for classification, extremity of values for regression, or purity of node for multi-output.

Pydotplus

PyDotPlus provides a Python Interface to Graphviz’s Dot language.

import pydotplus
from IPython.display import Image
graph = pydotplus.graph_from_dot_data(dot_data)
Image(graph.create_png())

The jupyter file to this notebook is here.

In the next blog, we will be doing our first project on building a decision tree from scratch in python.