How to Extract Decision Rules from Scikit-learn Decision-Tree?

If you have trained a decision tree using the scikit-learn library in Python and you want to extract the decision rules from it, there are several ways to do this. Decision trees are a popular machine learning algorithm for classification and regression tasks, and understanding the decision rules can provide valuable insights into how the model makes predictions.

One way to extract the decision rules from a scikit-learn decision tree is to use the export_text method. This method takes in the trained decision tree model and returns a textual representation of the decision rules. Here’s an example of how to use it:

from sklearn.tree import export_text

# assuming `tree` is your trained decision tree model
tree_rules = export_text(tree)
print(tree_rules)

Another option is to visualize the decision tree using the export_graphviz method and then use a tool like Graphviz to visually inspect the decision rules. Here’s how you can do this:

from sklearn.tree import export_graphviz
import graphviz

# assuming `tree` is your trained decision tree model
export_graphviz(tree, out_file='tree.dot', feature_names=feature_names)
with open("tree.dot") as f:
    dot_graph = f.read()
graphviz.Source(dot_graph)

Once you have visualized the decision tree, you can examine the nodes and edges to understand the decision rules. Each node represents a decision based on a specific feature, and the edges represent the possible outcomes of that decision. By following the paths from the root node to the leaf nodes, you can extract the decision rules that the model uses to make predictions.

It’s important to note that decision trees can become quite complex, especially with a large number of features or a deep tree. In these cases, the decision rules can be difficult to interpret and may not provide much value. It’s often helpful to simplify the decision tree or use other methods such as feature importances to gain insights into the model’s behavior.

In conclusion, extracting decision rules from a scikit-learn decision tree can be done using the export_text or export_graphviz methods. These decision rules can provide valuable insights into how the model makes predictions, but it’s important to consider the complexity of the tree and the interpretability of the rules when doing so.