I have a dataset with a target variable following Poisson distribution. It starts at 0 and goes until 30. But there are 8300 0's and only 2 30's.
From this data, I need to create a classification model. To do that, I need to decide what the range for each class should be. For instance:
The challenge is that I can train the model very well for the first bin but not that well for the others because of the distribution of the target variable. The only idea is to try different variations of classes and checking their performance. But this is not a time-efficient technique. Does anyone have any idea how to tackle this? Thanks!