# Updating One-Hot Encoding to account for new categories

My question is focused around how to appropriately update an encoded feature set when a new category is introduced by the test data. I use the data in logistic regression and I know it is not a 'live' model (i.e. gradient descent is performed whenever new data is introduced) but do I have to retrain the model to account for added features or do I just add it to subsequent test set values.

To exemplify the problem consider a TV Show training set where each show has a 'networks' feature set that includes one or more of the following:

["abc","cbs","nbc"]


Then, in the testing set there is a TV Show with the feature set:

["abc", "hulu"]


Would I have to add the new feature retroactively to the training data and retrain the model eventhough it will never occur? Wouldn't this introduce 'look-ahead-bias'?

How do I account for the added feature in the encoder going forward?