Thanks for the kind words and real-world example!

Regarding solutions, there's no single answer - it is highly dependent on the data issue. If the low accuracy is caused by data quality, the best solution is to collect more high quality data. If you can't collect more data, you can try bootstrapping or removing that data if you think it corrupts overall accuracy.

If the data is accurate, it's a good idea to figure out why the model can't fit your data. This often requires in-depth understanding of both the modeling technique and the subject. For a simple example, a linear model won't fit a non-linear relationship. Here, there is unfortunately no single solution for all cases.

One final point, the purpose of a model is to generalize. The technique in the post helps us increase variance and reduce bias. But, those low accuracy areas may not be problematic - they may be noise due to sampling and actually should exhibit poor accuracy. Just something to consider.