How can I get the names of the features after applying OneHotEncode in ColumnTransformer using Sklearn Pipeline?

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You can access the names of the features generated by OneHotEncode in ColumnTransformer using the get_feature_names() method. Here's an example:

from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder

# Define ColumnTransformer to apply OneHotEncoder to categorical features
preprocessor = ColumnTransformer(transformers=[('cat', OneHotEncoder(), ['gender', 'education'])])

# Define pipeline to apply preprocessor and other steps
pipeline = Pipeline(steps=[('preprocessor', preprocessor), ('classifier', LogisticRegression())])

# Fit pipeline to data
pipeline.fit(X_train, y_train)

# Get the names of the generated features
feature_names = pipeline.named_steps['preprocessor'].transformers_[0].named_steps['onehotencoder'].get_feature_names(['gender', 'education'])
print(feature_names)

In the example above, get_feature_names() is used to get the names of the generated features for the columns gender and education. The resulting feature_names list will contain the names of the generated features in the order they were created by the OneHotEncoder.