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:

from sklearn.tree import _tree

def tree_to_code(tree, feature_names):
    tree_ = tree.tree_
    feature_name = [
        feature_names[i] if i != _tree.TREE_UNDEFINED else "undefined!"
        for i in tree_.feature
    ]
    print("def tree({}):".format(", ".join(feature_names)))

    def recurse(node, depth):
        indent = "  " * depth
        if tree_.feature[node] != _tree.TREE_UNDEFINED:
            name = feature_name[node]
            threshold = tree_.threshold[node]
            print("{}if {}  {}".format(indent, name, threshold))
            recurse(tree_.children_right[node], depth + 1)
        else:
            print("{}return {}".format(indent, tree_.value[node]))

    recurse(0, 1)

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:

def tree(f0):
  if f0  1.5
      if f0  3.5
          return [[ 4.]]
      else:  # if f0 > 4.5
        return [[ 5.]]
  else:  # if f0 > 6.0
    if f0  7.5
        return [[ 8.]]
    else:  # if f0 > 8.5
      return [[ 9.]]

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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