Machine Learning with small set of positive outcomes

Both hxd1011 and Frank are right ( 1). Essentially resampling and/or cost-sensitive learning are the two main ways of getting around the problem of imbalanced data; third is to use kernel methods that sometimes might be less effected by the class imbalance. Let me stress that there is no silver-bullet solution. By definition you have one class that is represented inadequately in your samples. Having said the above I believe that you will find the algorithms SMOTE and ROSE very helpful.