# Ensemble Models and Partitioning Algorithms in SAS® Enterprise Miner - Ask the Expert Q&A

by on ‎06-07-2017 03:35 PM (1,412 Views)

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Did you miss the Ask the Expert session on Ensemble Models and Partitioning Algorithms in SAS® Enterprise Miner? Not to worry, you can catch it on-demand at your leisure.

Watch the webinar

The session covers Ensemble Models and Partitioning Algorithms in SAS® Enterprise Miner. The session covers:

• An introduction to ensemble models and why they can be a valuable tool for predictive modeling
• A review of decision trees and reveal a feature that makes partitioning algorithms such effective candidates for ensemble techniques
• Define Bagging and Boosting
• Discuss advantages and disadvantages for the following ensemble methods available in SAS Enterprise Miner
○ Random Forests
○ Stacked Ensembles

Here are some highlighted questions from the Q&A segment held at the end of the session for ease of reference.

Q: Can I use all model nodes with the Ensemble Node?

A: In SAS Enterprise Miner 14.2 the Ensemble node only supports the modeling nodes that generate score code in DATA step format. Not Memory Based Reasoning, HP Forest or HP Text Miner

Q: What if I have an interval target variable, can I use the Ensemble Node with it?

A: Yes, Ensemble Node works with either an interval target or categorical target variable

Q: Is there a maximum number of models that can be ensemble?

A: No there is no maximum, must have 1 or more model nodes proceeding the ensemble node.

Q: How does the voting combination method work for an interval target?

A: The voting method is only available for categorical target variables. When you use the voting method to compute the posterior probabilities, two methods are available for voting the posterior probabilities: Average and Proportion.

Q: When you get the end group, is the bootstrap samples already combined and averaged?

A: Yes. The End Groups node will function as a model node and present the final aggregated model.

Q: For Stacked Ensembles, do you first run all 4 models independently to pick the best model from each then merge?

A: Yes, then you merge the predictions for the 4 models and model using the predictions as inputs.

Q: How do we know which ensemble approach(average/stacking/cluster-based) we should use for the certain situation?

A: The great news with SAS Enterprise Miner you can use all and see which one works best for your data in your situation.