Hey all, first post!
I’ve got some data that I’ve been toying around with and the dependent variable is either class A, B, C,
When I build a tabular learner:
g = (TabularList.from_df(df=df, cat names=cat_variables, cont_names=cont_variables, procs=procs)
.databunch( defaults=defaults.device, bs = 8196)
learn. tabular_learner(g, layers=[10000,5000], ps=[0.001, 0.01], emb_drop=0.04, metrics=accuracy)
I obviously get accuracy/predictions that have 4 outputs: A, B, C, and None. It’s pretty accurate too, around 90%. However, my question is as follows: Is there a way to force tabular learner to only pick A or B? This works if I remove all C and None examples from the data, but I’d like to leave them in. They have value since this is a demographics based dataset, I just specifically want to avoid the scenario of the model predicting C or None.
Edit: I’ve got a lot of learning to do, so, I’m sorry if this is a dumb question!