About munitech4u

munitech4u · ‎03-14-2016

Well, I saw the log, one of the input variable has more than 32000 categories. I think that is the problem

munitech4u · ‎03-14-2016

I am trying to run a decision tree on a binary variable, with event rate around 2% on a 10m population in EM. The input variables can be continuous, categorical or character. But it is throwing me run time error. When I had ran it first on a different dataset with 4m population and 1.22% event rate, it ran smooth. I had even much larger set of input variables. What could be the possible reason? I do not see anything in log.

munitech4u · ‎03-04-2016

Regression: Train>0.012276, Test: 0.012101 SVM: Train>0.012515, Test: 0.012374 Decision Tree: Train>0.012245, Test: 0.012069 NN: Train: Train>0.01227, Test: 0.012095

munitech4u · ‎03-03-2016

I am trying to create ensemble of 3 models: 1. Regression, cumulative events percentage captured at 20%: 56.8 2. Neural Network, cumulative events percentage captured at 20%: 59.3 3. SVM, cumulative events captured at 20%: 54.01 3. Decision Tree, cumulative events captured at 20%: 48.05 Ensemble, cumulative results captured at 20%: 54.18 My target is binary. and In ensemble, I have set the posterior probability to maximum. So it will take the maximum probability out of all models, I believe.

munitech4u · ‎03-03-2016

It does not allow to change the role of the variables in decision tree, from the output of neural network. That option is freezed

munitech4u · ‎03-02-2016

I created a neural network, but I can't find any option within enterprise miner, where I can access the variable importance, similar to decision tree. I wanted to get to a final list of reduced variables. Like in regression, we keep only the most significant variables for final model building.

munitech4u · ‎02-29-2016

It says, run time error encountered, please check the log. But log doesn't show any error. I think towards the end I see below, which might be the reason: 47423 %let _EM_TREECONVERSION=0; 47424 data _null_; 47425 set EMWS1.EM_NODEID end=eof; 47426 where upcase(Component) ='DECISIONTREE' and CLASS = 'SASHELP.EMMODL.DECISIONTREE.CLASS'; 47427 if eof then call symput('_EM_TREECONVERSION', '1'); 47428 run; NOTE: There were 0 observations read from the data set EMWS1.EM_NODEID. WHERE (UPCASE(Component)='DECISIONTREE') and (CLASS='SASHELP.EMMODL.DECISIONTREE.CLASS');

munitech4u · ‎02-29-2016

I am trying to create an ensemble, but encoutered a run time error, while running through the neural network node in EM. How can i rectify it?

munitech4u · ‎02-29-2016

Attaching the log

munitech4u · ‎02-29-2016

well, I added the decisions node, but it doesn't seem to change the results. It shows the lift chart, but nothing much. How do I access the model accuracy? It doesn't even show the variable importance. But I remember, we can access the variable importance and ROC values in R and Python packages.

munitech4u · ‎02-26-2016

yes, the decision tree gives the tree with specified depth. Not sure, why gradient boosting fails to produce.

munitech4u · ‎02-26-2016

Ok, I changed the target variable to binary, It produced the lift chart, but no variable importance or tree? Is it even meaningful?

munitech4u · ‎02-24-2016

I am trying gradient boosting on a dataset of size 3.4m with event rate of 1.2%. I ran with following parameters: Max depth:10 surrogate rules:2 leaf fraction:0.05 But it fails to produce any result. blank output, no variable importance.

munitech4u · ‎02-24-2016

Even I am facing the same problem. Gradient boosting node is not able to produce any model, no plots,no variable importance. Though it ran successfully. I changed the following in default options: Max Depth: 10 Surrogate rule: 2 The decision tree is running fine, but not gradient boosting.

munitech4u · ‎07-24-2015

It is interesting, i removed the % from if condition and now, it seems to be working fine within macro. I also created 600 macro values each for upper and lower percentile and used the same condition, so basically it writes those many if statements with loop.

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