


How to Extract Decision Rules from a Scikit-Learn Decision Tree?
Extracting Decision Rules from Scikit-Learn Decision Tree
Decision trees, a widely used machine learning algorithm, provide insights by modeling decision-making processes as a hierarchical structure of rules. However, extracting these decision rules explicitly can be challenging. This article outlines a comprehensive approach to extracting textual decision rules from a trained Scikit-Learn decision tree.
Python Code for Decision Rule Extraction
The following Python code snippet utilizes the underlying data structures of Scikit-Learn decision trees to traverse and generate human-readable decision paths:
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Creating a Valid Python Function
This code traverses the tree recursively, printing out each conditional split and threshold. The result is a valid Python function that effectively emulates the decision-making process of the trained decision tree.
Example Output
For instance, consider a tree that attempts to return its input, a number between 0 and 10. The generated Python function would look like this:
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Benefits and Cautions
This method provides a clear and testable representation of the tree's decision rules. However, note that the code assumes that each node in the tree is a binary decision node. If your decision tree contains non-binary decision nodes, you will need to adapt the code accordingly.
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