I have a labelled training dataset DS1 with 1000 entries. The targets (True/False) are nearly balanced. With sklearn, I have tried several algorithms, of which the GradientBoostingClassifier works best with F-Score ~0.83.
Now, I have to apply the trained classifier on an unlabelled dataset DS2 with ~ 5 million entries (and same features). However, for DS2, the target distribution is expected to be highly unbalanced.
Is this a problem? Will the model reproduce the trained target distribution from DS1 when applied on DS2?
If yes, would another algorithm be more robust?