I am looking for some general advice on where to start with this problem.  There are 350 sparse (low positive integer) features.  I have 2000 positives, 1000 negatives, and infinite unlabeled data, where the estimated true positive rate in the unlabeled data is ~1% (but this isn't known for certain).  The goal is to predict the probability of an example being positive.How would you approach the problem if you had roughly 40 hours to attain the best possible model?What about if you had more time?
General strategy for imbalanced, semi-supervised, sparse problem
machine learning;predictive modeling;semi supervised learning