How to visualize a decision tree using scikit-learn Python

In this Kit, we will see how to visualize a decision tree using scikit-learn Python. A decision tree uses a tree representation to solve a problem in which each leaf node corresponds to a class label and attributes are represented on an internal node of the tree. We can represent any Boolean function on discrete attributes using a decision tree. There are four ways to visualize a decision tree in Python

print a text representation of the tree using the sklearn.tree.export_text method

plot with method sklearn.tree.plot_tree (requires matplotlib)

plot with sklearn.tree.export_graphviz method (graphviz required)

plot with dtreeviz package (requires dtreeviz and graphviz)

We use the sklearn.tree.export_graphviz method to visualize the decision tree in the example below

Please check below code how to visualize decision tree using scikit-learn Python

Fig: Preview of the output that you will get on running this code from your IDE

Code

Instructions

Follow the steps carefully to get the output easily..

1. Install scikit-learn by using 'pip install -U scikit-learn'
2. Install graphviz using 'pip install graphviz'
3. Copy the snippet using the 'copy' and paste it in your IDE
4. Run the file to generate the output.

I hope you found this useful. I have added version information in the following sections.

I found this code snippet by searching for ' visualize a decision tree in jupyter notebook' in kandi. You can try any such use case!

Environment tested

1. The solution is created in Jupyter notebook 6.5.2.
2. The solution is tested on Python 3.11.0

Using this solution, we are able to understand how to visualize a decision tree using scikit-learn Python with simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us how to visualize a decision tree using scikit-learn Python

Dependent Library

You can search for any dependent libraries like 'scikit-learn' .

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