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?
Updating One-Hot Encoding to account for new categories
machine learning;logistic regression;categorical data;recommender system
I think you have two options:Automate your train/test pipeline so that one-hot encoding is part ofit. If new categorical variables are introduced, they can be featuredin the training dataset even if not very prevalent. This wouldintroduce some bias if the nature of the TV show distribution haschanged over time (e.g. 20 years ago there weren't as many options)but I don't necessarily think it is a show stopper.If new possibilities are introduced over time but for whatever reason you can't  retrain, then you should omit using that new value. This has its own disadvantages because in your example, it would be a TV show with no network.