I have a dataset on which one of the features has a lot of different categorical values. Trying to use a LabelEncoder, OrdinalEncoder or a OneHotEncoder results in an error, since when splitting the data, the test set ends up having some values that are not present in the train set.
My question is: if I choose to encode my variables before splitting the data, does this cause data leakage?
I'm aware that I shouldn't perform any normalization or educated transformations on the data before splitting the dataset, but I couldn't find a solution for this problem inside scikit-learn.
Thanks in advance for any responses.
Edit: This particular features has very high cardinality, with around 60k possible values. So using scikit-learn's
handle_unknown set to
ignore would introduce too many new columns to the dataset.