I am currently working on text classifier with some pretty unique characteristics. The data is composed of about 2K categories but 98% of the data lives in just one of those 2K categories. However, our main problem is that we have a list of additional categories but have yet to see any transactions that fall into those categories (we know they will eventually).


1) Does it make sense to "prime" the model with these additional categories? For example, I could just add examples of these categories to my training set.

2) If priming is an acceptable are there any best practices or guides in doing so? I am wondering if I should just add the additional transactions to my training corpus, randomize them with other text, how many examples of the training set they should represent, etc..


This is not the answer you'll want to hear, but I would say, "no." Priming is not a good idea. A model is only as good as its inputs. If you're making up the inputs yourself, then your model isn't learning real patterns and you might as well just hard code a set of rules that represent how you would have created the primed data.

A fancier way might be to make a bayesian model where you create priors based on your assumptions. The bayesian model would then evolve as real data becomes available.

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