Dataset with disproportionately more of a single label than any other
There are a few ways:


Oversample the under-represented class.
issue: Leads to very constrained boundaries (not smooth) around the under-represented class
Weigh the loss coming from under-represented class more than over-represented class. If you think about it, this is mathematically doing the same thing as 1
Use SMOTE (here) (i recommend this). Basically samples additional points randomly from lines joining nearest under-represented classes and increases the dataset size accordingly. scikit contrib implementation of smote


There are other ways but they offer very marginal improvement over SMOTE