Drawing conclusions about cascade quality during training

I am wondering if someone could help me draw some conclusions about the quality of my HAAR cascades using the output of opencv_transcascade as they are being generated. The reason being a) I'm curious, b) I'd like to be able to stop wasting resources training on a cascade that is going to end up being 'funky'. Given a minhitrate of .99 and a false alarm rate of 0.5, properly curated positive and background samples with a 3:1 neg:pos ratio, I have seen: - cascades that complete at much earlier stage than expected. - stages where I don't see FA fall below 1 until N > 5 - small file size of final cascade.xml ( < 30kB) - cascades where the N (what is this signify?) column exceeds 40, yet others where it rarely exceeds 10 - things like this (single entry with a zero false alarm rate):

===== TRAINING 9-stage =====
POS count : consumed   2723 : 2817
NEG count : acceptanceRatio    6960 : 0.000287357
Precalculation time: 6
+----+---------+---------+
|  N |    HR   |    FA   |
+----+---------+---------+
|   1| 0.999265|        0|
+----+---------+---------+
END

So I'm asking, what do some of you pros look for in the training output that help you predict the general quality of - input settings - positive sample quality - background sample quality