Feature names from OneHotEncoder

python-3.x scikit-learn one-hot-encoding

You can pass the list with original column names to get_feature_names:

encoder.get_feature_names(['Sex', 'AgeGroup'])

will return:

['Sex_female', 'Sex_male', 'AgeGroup_0', 'AgeGroup_15', 'AgeGroup_30', 'AgeGroup_45', 'AgeGroup_60', 'AgeGroup_75']

python-3.x scikit-learn one-hot-encoding

column_name = encoder.get_feature_names(['Sex', 'AgeGroup'])one_hot_encoded_frame =  pd.DataFrame(train_X_encoded, columns= column_name)

python-3.x scikit-learn one-hot-encoding

Thanks for a nice solution. @NursnaazThe sparse matrix needs to convert into a dense matrix.

column_name = encoder.get_feature_names(['Sex', 'AgeGroup'])one_hot_encoded_frame =  pd.DataFrame(train_X_encoded.todense(), columns= column_name)

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