Thank you.  Now I can read those graphs.   Setting the X axis to predicted target and having it group the models in one chart produces a very useful graphic.   ... View more

Outside of the "inest=" issue, there is a lot of good information in your post.   Thanks for posting.   ... View more

>> There is not a way to pass initial parameters to the Regression node.        So you can't do this with SAS EM.  That doesn't make sense to me.   proc logistic data=mydata outest=parms; ... run; ... ... proc logistic data=mydata inest=parms; ... run;     ... View more

The usage note does not suggest building a model on a small sample, and then just using that.  It gives a technique for speeding up building a logistic regression model using the full data set.   From http://support.sas.com/kb/22/607.html Usage Note 22607: Preventing excessive time or memory use by PROC LOGISTIC   Initial parameters too far from final parameters  When the initial parameters are far from the final parameters, the procedure may need many iterations to reach the solution. While this generally isn't a problem when the model has a small number of parameters and the data set is not large, the time needed can become large when this is not true. You may be able to reduce the number of iterations needed, and therefore the time to fit the model, by using the following strategy: Fit the desired model using a relatively small, random subset of the data. Make sure that all levels of all CLASS variables appear in the subset. Use the OUTEST= option to save the parameter estimates. Using a small, random subset of the data that can be held in memory should require little time and allow you to get good starting values. Now you can run PROC LOGISTIC on your full data set with the INEST= option to use the saved estimates as starting values. By doing this, you are hopefully starting close to the solution so that fewer iterations will be necessary. Note that it is not possible to know in advance the number of iterations that will be needed to find the solution for any given data set and model. ... View more

Thanks for the offer.  At this point, unless I find a way to just hook it up (with a prior regression node) to speed up training, logistic regression is simply being beaten by every other modeling technique: Decision Trees, HP Trees, HP Forest, and Neural Networks - techniques that all quickly build models.     ... View more

You are saying something very different from what the usage note says.  You are saying the model is trained on a small set of data, and that model is the one used.   The usage note says the technique shortens the training time using the full data set.   Does Variable Selection, set for two-way interactions, accomplish the same thing that would be accomplished with logistic regression being set to two-way?   I have selected the relevant variables.  A couple of the class inputs have a large number of levels (200 and 60), which I have greatly reduced just for the logistic regression models, but they still don't build.  HP Trees, HP Forest, and Neural Networks all build very quickly, without reducing those levels.    I understand that the logistic regression technique gets exponentially larger with class variables with a high number of levels, as opposed to the other techniques.  That is why I was trying to "start closer" to the final parameters by implementing that usage note.   Anyway, I have moved on.  All of the other modeling techniques are simply beating logistic regression, the way I can run it.  (I've already used the Variable Selection node in front of it, but I still have to greatly reduce the number of levels of those two inputs for it to complete, and setting two-way interactions in the Regression node is a no-go.)         ... View more

>> But I think that you are asking about training a model with a few observations and using that model to score a larger data set.   No.   I am trying to implement the following usage note: "Usage Note 22607: Preventing excessive time or memory use by PROC LOGISTIC" http://support.sas.com/kb/22/607.html   This usage note says to train a logistic regression model using a small stratified sample of the data, and then to pass the parameter estimates to a regression model that uses the full data set, so it continues training from that point.  The idea is that by starting closer to the eventual estimate results, the logistic regression model using the full data set will be able to complete in a reasonable amount of time.   The parameter estimate table is listed as an output from the first regression table.  I just need to get the 2nd logistic regression node to use it, and to use the training/validation data from the main data source node.  (In SAS/STAT this appears to be simple, you use outest = x on the first regression model, and then inest  = x on the second one.)         ... View more

In SAS Studio, SAS Programmer is greyed out.  I can select "New SAS Program." I our previous course we learned to use SAS EM.  If SAS/STAT is available, I would have much preferred to learn that.  (My undergrad degree is in Computer Science.)    If SAS/STAT is available, learning how to completely rebuild my project using that is outside of the scope of this project.     ... View more

Thanks.  I was able to find an SAS/STAT example, which I included in my other other post.  I don't have access to SAS/STAT.   I have access to SAS Enterprise Miner 14.2 (OnDemand), SAS Enterprise Guide (which I haven't installed), and SAS Studio.   I attempted to export the Logistic Regression node as a model, so I could edit the code to add 'inest', and then import the code as a new model, however the code is in an entirely different format that the SAS/STAT examples, and there isn't even a call to reg, dmreg, etc.    My other post describes my attempts to drop the Train and Validate tables from the first regression node, and to assign the parameter estimates, which exist, in such a way as the next node uses them.   I just need an example where someone uses the parameter estimates in the next node, so I can adapt that to my variable/table names.  All of my Google searches for the elements such an example would contain have come up empty.   ... View more

The instructor is not an SAS expert.  The course allows students to use the products they choose, whether it be R, SAS EM, Python, IBM Watson Analytics, etc.       ... View more