# Dataset with disproportionately more of a single label than any other

I'm using the data analysis software Orange to analyze rows of data with labels $\{H, T,L\}$. $T$ is the neutral state of the system I'm trying to model so data is almost always labeled with $T$. This leads to an extremely high $97$% classification accuracy, that is untrue. Almost no datapoint with an $H, L$ actually gets labeled with an $H, L$ respectively by the neural net. The neural net is doing what's logical: flatten everything to $T$ since that occurs most of the time. How can I repair this situation?

• There are downsampling and oversampling approaches to mitigate the imbalance. There are also cost-sensitive approaches, but I'm not sure if they can be applied with neural networks. Also consider computing recall metrics, they should be very low. – Gabriel Romon Aug 27 '18 at 8:07