Name of variables in sklearn pipeline

Two key components can help make this work. The first gets the encoding names from the OneHotEncoder: OneHotEncoder.get_feature_names_out. Specifically, you use that on your encoder as encoder.get_feature_names_out(). The second component is that sklearn.tree.export_text takes a feature_names argument. So, you can pass those extracted names right into the display system. Other sklearn tree displayers also take that parameter (plot_tree, export_graphviz).

See here for related SO:

1. Feature names from OneHotEncoder
2. How to display feature names in sklearn decision tree?

sklearn docs here:

1. https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html
2. https://scikit-learn.org/stable/modules/classes.html#module-sklearn.tree (follow those links for the tree export/plot functions).

The following should work for you (Edit: I forget the pipeline part in my example. You can use my_pipe.named_steps[step_name] to extract out the OneHotEncoder. You may have to nest that since you have nested pipelines. Added that example below.):

import pandas as pd
import numpy as np
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import DecisionTreeClassifier, export_text

import sklearn
print(sklearn.__version__)  # ---> 1.0.2 for me

ftrs = pd.DataFrame({'Sex'     : ['male', 'female']*3, 
                     'AgeGroup': ['0-20', '0-20', 
                                  '20-60', '20-60',
                                  '80+', '80+']})
tgt  = np.array([1, 1, 1, 1, 0, 1])
encoder = OneHotEncoder()
enc_ftrs = encoder.fit_transform(ftrs)
dtc = DecisionTreeClassifier().fit(enc_ftrs, tgt)

encoder_names = encoder.get_feature_names_out()
print(export_text(dtc, feature_names = list(encoder_names)))

Which for me gives the following output:

|--- AgeGroup_80+ <= 0.50
|   |--- class: 1
|--- AgeGroup_80+ >  0.50
|   |--- Sex_female <= 0.50
|   |   |--- class: 0
|   |--- Sex_female >  0.50
|   |   |--- class: 1

Including the pipeline, it looks like this:

from sklearn.pipeline import Pipeline
pipe = Pipeline([('enc', OneHotEncoder()),
                 ('dtc', DecisionTreeClassifier())])
pipe.fit(ftrs, tgt)
feature_names = list(pipe.named_steps['enc'].get_feature_names_out())
print(export_text(pipe.named_steps['dtc'],
                  feature_names = feature_names))

with the same output.