Hello,   To me, it's all in feature engineering. That's where the gain lies.   If you search for SAS and "feature engineering" or "feature building", you find a lot of hits on Internet.   Also, Model Studio has some nodes for it.   Thanks, Koen ... View more

Hello @Jonison ,   Technically speaking, you want to conduct a multivariate multiple RF.  This RF is "multivariate" because there is more than one outcome variable (several dependent variables).  It is a "multiple" RF because there is more than one predictor variable.   There are several statistics and econometrics procedures in SAS that can simultaneously deal with more than one outcome variable (2 or 2+ outcome variables). You can of course use those procedures in a code node of SAS Enterprise Miner or Model Studio but you may want to avoid that. 😊   I'm going to have a look tomorrow at which standard nodes in EM support multiple dependent variables (simultaneously). I don't know offhand, but random forest(s) probably doesn't (you're right about that).   But all nodes based on neural networks certainly do! The output layer of the NN can have multiple nodes (and I am not talking about a multi-nomial or ordinal response, but multiple continuous / interval-scaled responses).   Good night, Koen ... View more

@Jonison wrote: Hello, experts, the official PLS procedure manual shows that the prediction of new data is performed by combining training data and new data (new data dont have value of responses). I did experiment, say 200 data items for model training, and 2000 new data items for prediction. The results are quite strange that the 2000 predicted results are quite similar (variability is less than 0.3), which is not right for sure.  When someone claims they are right and SAS is wrong, I believe SAS. So, you really really really need to demonstrate facts that support your claim. In other words, the burden is on you ... the burden is not on us to defend/explain the answers from SAS.   Also, in your earlier thread, I showed how to get predicted values from PROC SCORE, they match the predicted values from PROC PLS to within round-off error.   Start by showing us the code used to create the input data set (the data set that has the training data and the new observations with missing response values), the code used in PROC PLS, and (a portion of) the data used (using the instructions given by @ballardw ). ... View more

You can get the loadings for each factor as well. ... View more

If it works as expected when you manually run the .vbs file, you are very close to having it working via scheduling.  Another thing to check is to make sure you are using the proper version of cscript.exe that matches your EG bitness.  For example, if you are using 32-bit EG, then the program/script on the Action tab in your Windows Scheduled task should be: C:\Windows\SysWOW64\cscript.exe   If you are using 64-bit EG, it should be: C:\Windows\System32\cscript.exe   In both cases, the "Add arguments" field should contain the quoted full path to your .vbs file.  For example: "C:\fullPathToVBSFile\EGScript1.vbs"   Casey ... View more

I find deleting rows via SQL passthru is efficient: proc sql; connect to Oracle ( <Oracle connection details> ); execute (delete from MyOracleDatabase.MyOracleSchema.MyOracleTable ) by Oracle; disconnect from Oracle; quit; I also find PROC DATASETS APPEND is good for loading data, but you normally need to tweak the INSERTBUFF = option to get good loading performance: libname MyOracle Oracle <Oracle connection details> insertbuff = 10000; proc datasets library = MyOracle nolist; append base = MyOracleTable data = MySASLib.MySASTable; run; quit; ... View more

https://blogs.sas.com/content/iml/2017/06/21/jackknife-estimate-standard-error-sas.html ... View more

Hi, may I know if you found the solution for this missing diagram? I am facing the same problem and it is urgent. Thanks ... View more

@Jonison wrote: The number of factors of model has been optimized selected, Then you don't need cross-validation any more. You just need the jackknife. ... View more