Interpreting Decision Tree Output

tags: #ML/supervised/classification/trees

To build a graphical representation of a decision tree in Python, you can use the graphviz library along with the scikit-learn library for creating and visualizing decision trees.

Note: Ensure that graphviz is installed

# install graphviz
pip install graphviz

Sample code for building a decision tree graph:

import graphviz
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree

# Assuming you have a pandas DataFrame called df
X = df[[list_of_features]] 
y = df["target_column"]  

# Create a decision tree classifier
clf = DecisionTreeClassifier()
clf.fit(X, y)

# Visualize the decision tree
dot_data = tree.export_graphviz(clf, out_file=None,
                                feature_names=df.columns, # list of features
                                class_names=df["target_column"].unique(),
                                filled=True, rounded=True,
                                special_characters=True)

graph = graphviz.Source(dot_data)
graph.render("decision_tree")  # Save the tree as a PDF or any other format

# Display the decision tree
graph.view()

Interpreting the Decision Tree