Hi Izel,
I would recommend that you should follow the standard principles of
training and replication. The goal is generally to determine whether a
model that is trained on one portion of your data replicates in an
independent portion of your data (i.e. displays good fit, produces
consistent estimates, etc...). If it does, then it would make sense to
carry forward in the complete dataset. Or perhaps only carry forward a
version of the model with nonzero parameter estimates that replicated
across independent subsets of the data. I don't see how training on
the full dataset comes into play, as that leaves no remaining
independent sample to replicate in...
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