How do I find which attributes my tree splits on, when using scikit-learn?

Question

I have been exploring scikit-learn, making decision trees with both entropy and gini splitting criteria, and exploring the differences.

My question, is how can I \"o

Answer 1

Scikit learn introduced a delicious new method called export_text in version 0.21 (May 2019) to view all the rules from a tree. Documentation here.

Once you've fit your model, you just need two lines of code. First, import export_text:

from sklearn.tree.export import export_text

Second, create an object that will contain your rules. To make the rules look more readable, use the feature_names argument and pass a list of your feature names. For example, if your model is called model and your features are named in a dataframe called X_train, you could create an object called tree_rules:

tree_rules = export_text(model, feature_names=list(X_train))

Then just print or save tree_rules. Your output will look like this:

|--- Age <= 0.63
|   |--- EstimatedSalary <= 0.61
|   |   |--- Age <= -0.16
|   |   |   |--- class: 0
|   |   |--- Age >  -0.16
|   |   |   |--- EstimatedSalary <= -0.06
|   |   |   |   |--- class: 0
|   |   |   |--- EstimatedSalary >  -0.06
|   |   |   |   |--- EstimatedSalary <= 0.40
|   |   |   |   |   |--- EstimatedSalary <= 0.03
|   |   |   |   |   |   |--- class: 1