How Can We Programmatically Extract Decision Rules from Scikit-Learn Decision Trees While Avoiding Common Pitfalls?

Extracting Decision Rules from Scikit-Learn Decision Trees

In machine learning, decision trees are commonly used to capture decision-making processes in the form of decision rules. These rules can be represented as textual lists, providing a clear understanding of the underlying logic in a decision tree.

Extracting Decision Rules Programmatically

The Python function tree_to_code enables the extraction of decision rules from a trained decision tree. It takes as input the trained tree and a list of feature names, and generates a valid Python function that represents the decision rules.

<code class="python">def tree_to_code(tree, feature_names):
    # ...</code>

The generated function has the same structure as the decision tree, using nested if-else statements to represent the decision paths. When provided the input data, the function returns the corresponding output.

Example Output

For a decision tree that tries to return its input (a number between 0 and 10), the generated code might look like:

<code class="python">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.]]</code>

Limitations of Other Approaches

Some common pitfalls in extracting decision rules from decision trees include:

• Mistakenly using tree_.threshold == -2 to identify leaf nodes (not always reliable)
• Including unnecessary multiple if-else statements in the recursive function
• Crashing due to leaf nodes having a feature value of -2

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