Machine learning testing data
No, your model isn't supposed to know about your test data, if you include clues in your training about what's in your test data, you will do something called
Data Leakage.
Data leakage would lead to Over-fitting which will give you good results on that particular test set, but won't generalize to other data.
Lets say, you deploy this model in production and feed into it real-life data that it never encountered before, the predictions will be below the expectations you had in the training/testing phases because of those two phenomenons i mentioned.
I suggest you tweak a bit more your model during the training phase, maybe clean your data more, do something called OverSampling and UnderSampling if the target classes are imbalanced (You have for example 90% / 10% proportions in your training dataset), pick better features etc..
In conclusion: adjusting your model to have good predictions on your test data, in particular, is not good practice and will lead to creating a bad model on unseen data.