Hello everyone,
I have the following scenario. All suggestions
and pointers appreciated.
A model was developed on one set of dataset.
The model was applied to a different dataset
(different in the sense that the records were
obtained 6 months later).
Now using the old model, predictions are generated
using the new set of variables.
The problem is: Compared to actual values, the
predicted values are smaller -- i.e. there is
underprediction.
The question: which is the "best" way to adjust
the predictions i.e. to minimize the gap between
predicted and actual values?
If at all possible, I prefer not to run a new
regression.
Thanks for all pointers.
V.
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