Python for Finance Cookbook

Specifying possible categories for OneHotEncoder

When creating ColumnTransformer, we could have additionally provided a list of possible categories for all the considered features. A simplified example follows:

one_hot_encoder = OneHotEncoder(    categories=[['Male', 'Female', 'Unknown']],     sparse=False,     handle_unknown='error',     drop='first')one_hot_transformer = ColumnTransformer(    [("one_hot", one_hot_encoder, ['sex'])])one_hot_transformer.fit(X_train)one_hot_transformer.get_feature_names()#['one_hot__x0_Female', 'one_hot__x0_Unknown']

By passing a list (of lists) containing possible categories for each feature, we are taking into account the possibility that the specific value does not appear in the training set, but might appear in the test ...