My dataset is 6.3 million observations, with 150 features for each one. 25 000 of these observations are positive case and the rest is negative case, so about 1:250 class balance.
I've been training GBM models on it (6-fold CV), with 1000 trees, 10 leaves per tree and I've changed the minimum number of observations per leaf parameter. I've tried 1, 10, 50, 100 and 500 so far and the AUC does not seem to change much. AUC has actually gone up from .885 to .900, but that could be due to the random seed picked or whatever.
I feel like at this point I should be drawing some conclusions about something. I previously had a feeling that since there's just 4200 positive cases per fold, I'd need the minimum observations per leaf to be small or it wouldn't capture anything. But after these results that's apparently not the case, so my understanding of how GBM works is probably flawed.
It could be that my choice of number of trees or number of leaves is completely wrong though, but since it takes an hour to train each model, I haven't gotten to trying wide range of these parameters yet.
Could anyone offer their thoughts on this situation?