SAS Viya VDMML Solution with By Variable

[C_Golen]

Hi to all!

I have a data set on SAS Viya version 3.5. I set up an ml model to predict how much the specific product will be sold for each store in my data set. However, while setting up this model, I want to give "By Variable" just like in the forecast studio. In other words, I want to model on a store-item basis with ML solution.

Previously in Enterprise Miner we could do this with start end group. How can we do it in Viya? Also, how to do K-fold cross validation in SAS Viya?

Ref: https://communities.sas.com/t5/SAS-Communities-Library/How-to-calculate-cross-validation-error-using...

Thanks in advance!

[sbxkoenk]

Hello,

I am afraid that this "Start Group - End Group" functionality from Enterprise Miner has disappeared in VDMML (when using Model Studio front-end app). I guess it's on the to-do list of Model Studio R&D.

With regard to Cross-Validation (CV) :
Are you wondering how to perform cross-validation inside a Model Studio VDMML pipeline?

In Enterprise Miner I was also using "Start Group - End Group" functionality to achieve that.

Haven't tried to do CV in Model Studio yet.

Outside Model Studio , ... in SAS Studio ... , CV is easy to program.
Let me know if you want an easy program to create the k folds.
See also this :

crossValidateML action in SAS Viya 3.5
https://go.documentation.sas.com/doc/en/pgmsascdc/9.4_3.5/casactml/casactml_mltools_details02.htm

Here's an example of its usage :

SAS Help Center: Example 20.1 Cross Validation of a Forest Model

Koen

[C_Golen]

Hi Koen,

Many thanks for your interest.

Sorry to hear that 😕
My main goal is use it in the front - end. So, it would be great if I can manage to add this option to our pipeline.

[sbxkoenk]

Hello,

What exactly do you want the cross-validation for? :

- to honestly estimate the generalization error of the model (detect over-fitting)

- to find the best hyper-parameters

- to ...

What type of model do you want to fit?

• Cross validation is offered within gradient boosting as part of the VDMML.
• Both gradient boosting and forest models in SAS Model Studio allow for hyper-parameter optimization and cross validation to optimize model properties.

Koen