About JakesVenter

JakesVenter · ‎03-06-2017

Good morning everyone, I just wanted to find out if it is possible to specify upper and/or lower bounds on regression coefficients in SAS's proc logistic procedure? For example, all coefficients should be greater than 0 or all coefficients should be less than 0, etc? Thanks a lot! Kind regards, J

JakesVenter · ‎03-02-2017

I don't know if there are other analysts out there who struggle as much with Enterprise Miner's Interactive Grouping Node as much as we do. My team and I need to use this node regularly in E-Miner (version 13.2) in order to conduct our work, yet this thing breaks so much and gives errors on a regular basis - which is really frustrating. Currently, I need to use the node with a special codes dataset. The special codes dataset I import in E-miner have the exact same format as previous special codes datasets that I have used with great success (and it also has the same format as in E-miner's help file). When I run the IGN node with the special codes dataset and using the Quantile binning method (which is the method I prefer), the node stops and gives me an error. When I inspect the log, I find an error stating "Error: there was one unclosed do block". How do you even fix that if it is not you coding?? Anyway, when I run the node without the special codes dataset, it works fine. On the opposite side, when I run the node along with the special codes dataset and select the Bucket binning method instead, it also works fine. So, from what I understand, using the combination of a special codes dataset and the Quantile binning method results in this thing giving an error. And it is not as if E-miner's log-file is easy to inspect so that I can determine what the error is. Has anyone experienced similar problems and/or found ways to fix it? Thanks J

JakesVenter · ‎09-01-2016

Hi Mohammad, In addition to what you must do in my previous message, there is also something else that you must do in the code for the RF to work in EG. There should be a file called "OUTMDLFILE.bin" that is generated by the HPDM RF node in Eminer and saved in the folder where your EM project is saved. It should be saved under the "HPDMForest" folder that is located under your "EMWS" folder under the Workspaces folder. Now, in the SAS code, create a macro variable called path and let that macro variable reference this OUTMDLFILE.bin file. For example, something like %let path = "C:\Users\Desktop\OUTMDLFILE.bin" (the path would look something like this if I copied this file to my desktop - it doesn't matter, just as long as you reference this file). Then, again go to the place in the code where the macro em_hpfst_score is defined. Within that macro, reference the path macro variable at the place in the code that I highlighted in the picture attached to this message.

JakesVenter · ‎09-01-2016

Hi Mohammad One of the SAS consultants at our company helped me to run the Random Forest in EG. When you extract the RF scoring code and open it in EG, look for somewhere in the code where it declares a macro called %em_hpfst_score. In this macro, the code is looking for a value for the macro variable %em_score_output, but if we just extract the code from EM, it does not populate it by default. So all I did was in the SAS line above where the macro is being declared, I specified the macro variable em_score_output by typing the code let em_score_output = scoreset; where scoreset is the dataset I'm scoring the RF on. Just remember to put the part where the code actually scores the data with the RF within a datastep. I hope this makes sense - let me know if it worked!

JakesVenter · ‎02-09-2016

Thanks for the help Koen! I will have a look at the different techniques you mentioned and see which one shows the best performance/classification.

JakesVenter · ‎02-08-2016

Hi guys, I recently built a scorecard model using SAS E-miner's credit scoring node. The scorecard proved to be very good with excellent gini values and the rank ordering of the scores in terms of the events/non-events was more than satisifactory. However, the event modelled is extremely rare. In the sample, the events (1's) account for roughly 30%, whereas the true population proportion is 0.15%. The person who will be using the model is completely focussed on wanting a cut-off below which all events will be classified as 1's when predicting. The problem I face now is that - because of the rare event - the model DOES accurately capture a large percentage of event's below certain cut-offs when running it on out-of-time data, but it has an extremely large False Positive Rate as well, due to the large number of non-events in the population. I have adjusted the regression intercept for oversampling, but this does not seem to do much i.t.v. of cut-offs. Is there perhaps any techniques that you would recommend? Thanks!!

JakesVenter · ‎11-20-2015

Hi Miguel, Thanks for the interest in my question. I did manage to get the code runnning in Base SAS / SAS EG by copying the OUTMDLFILE.bin to a certain location on our company's grid processor and referencing it in my code. The reason why I asked the question that I did was: I applied some transformations to the variables before running variable selection / model nodes. When I extracted the code, I saw that some of these variables that I did not want in the model still appeared in the Base SAS code when applying the transformations. I am going to have a look at the Optimized code like Jason suggested, as I admit that might be where my problem is. Eventhough I know exactly how RF models work from a theoretical/academic point of view and I have been involved with some of them in R, I have no idea how the hp4score node in SAS works 🙂 I am doing what any analyst should not do and trusting in the results that the Base SAS code gives me which I got from the E-miner Scoring node. Thanks for the help!

JakesVenter · ‎11-20-2015

Hi Jason, Thanks for the info! I will definately be trying the variable selection in the RF node, since my company is running the latest SAS Packages. I am actually not a fan of the more complex models such as the RF node, simply because of its difficulty to explain (especially to my technical commity 🙂 ) and the fact that it does not score in a simple data step such as the other "lament" models (like the regression, neural net, etc.). However, in some cases - like the problem I have been dealing with now - it outperforms the other models by a significant margin. The most important part I took away from your message is to go and look at the optimized code. I did manage to get the model to run in Base SAS by using a score node. The main reason for me posting this question was that I saw some of those variables I rejected coming into the variable transformations bit of the code - something you addressed in the reply. Thanks for the help!

JakesVenter · ‎11-19-2015

Hi guys, I was hoping one of you could help me with this. I am familiar with Random Forests and how they operate (well, theoretically) but I'm struggling with the forests in E-miner. I want to model a certain dataset, but there are a few variables in the dataset that I don't want the model to use (i.e. I've discovered that - after runnning some exploratory nodes - some cause high correlations whilst others are unstable). I have dragged a metadata node into the diagram which I ran before the random forest node. In this metadata node, I set the role of these variables to "rejected". Additionally, just to be safe, when I right-click on the random forest node and select the "edit variables" option, I also set the "Use" colomn for these variables to "No" in the node itself. When I view the results, I see that these variables are not listed under "Variable importance" and they are also not listed when you select "view -> properties -> variables". Awesome! However, when I extract the scoring code to run the forest in Base SAS, I see that a lot of these variables that I don't want in the model are still run in the SAS code? Is this just the way the random forest node executes? Are these variables used in the final model anyways (which is not ideal)? Thanks for the help! J

JakesVenter · ‎10-13-2014

Thanks Miguel, I have been using Decision Tree Nodes in EM 6.1 as surrogate models to check the output of the rule induction node. Thus far it seems to do the trick. I just thought there might be an easier point-and-click method. Thanks for the help!

JakesVenter · ‎10-10-2014

Hi Hussein, I agree with Miguel and is even using the book from the course he recommends above in my day-to-day work. From my perspective, PCA is an awesome and great way to reduce the dimensions, although the problem with the technique is that it often becomes difficult to explain to a client or user of the model what exactly the PCA variables mean and how they relate to the parameters of the model. I would maybe use a decision tree to select useful variables and go from there (there's a specific way to configure the decision tree node to select variables). You can also use regression to select variables (especially forward selection, which is good at detecting strong interactions). Just note that these nodes may run a considerable amount of time due to your big dataset. Also (something I use a lot), since your inputs are interval variables, you could cluster them into groups using eminer's clustering node and then use one representative from each cluster for subsequent modelling. Eminer automatically exports the cluster representatives if you set the Variable Selection option in the clustering node to "Best Variables". Something I also sometimes use (although it's not as successful as the other techniques mentioned earlier) is SAS's variable selection node and then setting the minimum Chi Square lower bound much lower than Eminer's default value so that it may select a larger number of important variables. Hope you succeed! Jacques

JakesVenter · ‎10-10-2014

Hi Kanyange, You can use this equation: P_i** = ( P_i* x R_0 x P_1) / ( (1-P_i*) (R_1)(P_0) + (P_i*)(R_0)(P_1) ) where: P_i* is the unadjusted probability you get from your model R_0 and R_1 are the sample proportions of 1 and 0 respectively P_0 and P_1 are the original event and non_event rates (population rates) P_i** is the true probability

JakesVenter · ‎10-10-2014

Hi guys, Can anyone please tell me how to see the decision tree (the tree diagram with the leaves and so on) Eminer creates when you execute the rule induction node in Eminer version 6.1? When I access the results, I only seem to find information on the performance measures such as the misclassification rate, ASE, etc. Thanks guys!

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