How to Extract Decision Rules from Scikit-Learn Decision Trees in Python?

Extracting Decision Rules from Scikit-Learn Decision Trees

Extracting the underlying decision rules from a trained decision tree can provide valuable insights into its decision-making process. Here's how to do it in a textual list format using Python.

Python Function:

<code class="python">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_left[node], depth + 1)
            print("{}else:  # if {} > {}".format(indent, name, threshold) + depth)
            recurse(tree_.children_right[node], depth + 1)
        else:
            print("{}return {}".format(indent, tree_.value[node]))

    recurse(0, 1)</code>

Example Usage:

<code class="python">tree_model = DecisionTreeClassifier().fit(X, y)
tree_to_code(tree_model, feature_names)</code>

This function iteratively traverses the tree structure, printing out decision rules for each branch as it encounters them. It handles both leaf nodes and non-leaf nodes and generates a valid Python function that encapsulates the decision-making process of the tree.

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