02-10-2017 04:47 PM
I'm on SAS Enterprise Miner v 14.1. I have a fairly simple Decision tree model. The target is a binary variable (1,0) and I have about 380K observations. The data source is a SAS data set, and I have about 40 or so variables (Interval, Binary, Nominal).
The way enterprise miner is behaving is intermittent. Sometimes, I get a full blown decision tree and then when I make a small change to the Decision tree parameter (for. e.g change it from maximum depth of 6 to 4, I just a single node in the output. It basically does not produce any results what so over. The same model worked great before, until I changed the underlying data and now it behaves erratically.
I'm attaching the properties of the Decision tree node. Most of them are the default.
Anyone encounter this before? Know how to solve it?
02-10-2017 05:08 PM
The same model worked great before, until I changed the underlying data and now it behaves erratically.
How does the data compare to before?
When it does the one node, how predictive is it? Is that one variable correct? if only one variable is significant it's going to be highly predictive, which means it's possible that it may be a measurement error, or it's something that's only known after the fact.
I wonder if your priors are severly skewed, ie how many 1 vs 0 do you have in your 380K observations?
02-10-2017 05:17 PM
No, its not a data issue and not what you're thinking. There isn't one variable that is predictive. In fact the output is just one node showing my target variable distribution. None of the input variables. This happens when I reduce the depth of the tree from 6 to 5 or 4.
That one property change results in no output. Can't figure out why when it worked yesterday. The underlying data changes slightly. The same variables/features/target are used here too. Just the data itself changed and not that drastically.