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I had a similar situation and the solution I came up with was calculating
the standard deviation of the predictions of all the individual trees.
I found that when I trained my regressor on the lower half of my data, then
used the model to predict the upper half of my data my model generally
return
I've been wrestling with this same issue in the regression case.
I realize it's not as straight forward to balance continuous data as it is
for discrete classes of output.
But I wonder if this list has any thoughts about how it might be approached.
The data I'm predicting is distributed normally
