# Improve performance for known unlabelled test set

I'm training a machine learning model from a training set of $$1,000$$ samples (but around $$23,000$$ features). I'm fairly content with my cross-validated results, but would like to improve it further by using information I have about the test set I will be using.

To explain that some more: I have an unlabelled test set with $$500$$ unlabelled samples which I know is the one my model needs to perform on. I was wondering if there are some techniques which allow one to weight features based on some criterion, for example whether they are significant in the test set (or alternatively, find which samples in the train set are "close" to the ones in the test set, and try to fit them with a higher importance than the other, more distant samples). In case there is such a technique, I'd also be interested in a way to do this in sklearn in particular (and preferably as part of a pipeline so that I could use it in cross-validation as well, since it is very important that I be able to gauge the accuracy of my model).

• You say that your test set contains unlabelled samples. How can it be a test set if the samples are unlabelled? May 5 '20 at 21:53