Hi All,
We are doing comparison of SAS E-Miner projects to SAS Viya VDMML pipeline. We found that there is no node-to-node mapping available. Please refer attached snip for reference taken from Migration status document 2023.09.
Can someone please tell us what will be alternative in Viya VDMML for these E-Miner nodes - Append, Data Partition, Principal Components etc (there are few more but listed down here limited nodes). If there is any mapping available?
Thank in advance!!
Sorry, I do not open attachments.
There are not so many Enterprise Miner nodes.
Here they are by the way:
Getting Started with SAS® Enterprise Miner™ 15.3
About Nodes
https://go.documentation.sas.com/doc/en/emgsj/15.3/n1cpd0rgpneqwqn16mfcxp4sbjsb.htm
Can't you put your list as part of the text?
There's no 1-to-1 mapping
, but most of the Enterprise Miner capabilities are also there in Model Studio VDMML.
If you are unsure about an Enterprise Miner node, please ask ... and I will tell you how the same can be done / achieved in Model Studio VDMML.
BR,
Koen
Thank you very much for both of your comments!!
We are trying to create below node wise mapping of E-Miner nodes and its Viya alternative. Basis on your inputs, updated the same for Append, SOM/Kohonen and Partial Least Squares.
Also could you please validate this list for nodes to which we found mapping in Viya and explain for some nodes there is not direct mapping available but can be done in another way in Viya, is it possible to share that information (procedure/steps) over here?
Please Note - In below list, some nodes are in Magenta color is because we don't find it's direct Viya mapping but feature wise we feel that it could be alternative (we might be wrong in mapping some of those)
E-Miner Node | Viya node |
Append | Not available |
Data Partition | |
File Import | Score Code Import |
Filter | Filtering |
Input Data | |
Merge | |
Sample | |
Association | |
Cluster | Clustering |
DMDB | |
Graph Explorer | |
Link Analysis | |
Market Basket | |
MultiPlot | |
Path Analysis | |
SOM/Kohonen | Not available |
StatExplore | Data Exploration |
Variable Clustering | Variable Clustering |
Variable Selection | Variable Selection |
Drop | |
Impute | Imputation |
Interactive Binning | Transformations |
Principal Components | |
Replacement | Replacement |
Rules Builder | |
Transform Variables | Transformations |
AutoNeural | Neural Network |
Decision Tree | Decision Tree |
Dmine Regression | Linear Regression |
DMNeural | |
Ensemble | Ensemble |
Gradient Boosting | Gradient Boosting |
LARS | |
MBR | |
Model Import | |
Neural Network | Neural Network |
Partial Least Squares | Not available |
Regression | Linear Regression, Logistic Regression |
Rule Induction | |
TwoStage | |
Cutoff | |
Decisions | |
Model Comparison | |
Score | Score Data |
Segment Profile | Segment Profile |
Control Point | |
End Groups | |
Ext Demo | |
Metadata | |
Open Source Integration | Open Source Code |
Register Model | |
Reporter | |
SAS Code | SAS Code |
SAS Viya Code | Open Source Code |
Save Data | Save Data (Data Mining & Machine Learning) |
Score Code Export | |
Start Groups | |
HP BN Classifier | Bayesian Network |
HP Cluster | |
HP Data Partition | |
HP Explore | Data Exploration |
HP Forest | |
HP GLM | GLM |
HP Impute | Imputation |
HP Neural | Neural Network |
HP Principal Component | Feature Extraction |
HP Regression | Linear Regression, Logistic Regression |
HP SVM | Feature Extraction |
HP Text Miner | Text Mining |
HP Transform | Transformations |
HP Tree | |
HP Variable Selection | Variable Selection |
Incremental Response | |
Survival | |
Text Cluster | |
Text Filter | Filtering |
Text Import | |
Text Parsing | Text Mining |
Text Profile | |
Text Rule Builder | |
Text Topic | Categories |
TS Correlation | |
TS Data Preparation | |
TS Decomposition | |
TS Dimension Reduction | |
TS Exponential Smoothing | |
TS Similarity |
Thanks once again for your help!
Registration is now open for SAS Innovate 2025 , our biggest and most exciting global event of the year! Join us in Orlando, FL, May 6-9.
Sign up by Dec. 31 to get the 2024 rate of just $495.
Register now!
Use this tutorial as a handy guide to weigh the pros and cons of these commonly used machine learning algorithms.
Find more tutorials on the SAS Users YouTube channel.