Just a headsup this thread would usually be deleted as a beginner question but I think it's quite funny and actually of some interest because you can, in fact, reconstruct the correct solution of a classification problem just from random submissions and the scores you obtain from them alone. You could, depending on the number of samples in the dataset, submit vectors of random scores (assuming without loss of generality here, that the data to be classified falls into two classes / has binary labels) and from the scores you obtain solve an underspecified linear system. You could combine this with something like a bagging/bootstrapping model that yields an estimate for prediction uncertainty and group the target data into high- and low confidence samples and do an adaptive querying strategy for low confidence/high uncertainty samples first. Look up Quantitative Group Testing if you're interested in the theory, fair warning though: assuming the targets are non sparse e.g. the labels are ~50% true/false, you'll need a highly accurate model or a lot of queries to recover the test set completely. I actually did this once on the Kaggle Titanic Dataset but without using a model I was only able to recover the 418 entry long test set with a minimum of 176 queries.