Advanced Predictive Modeling Using SAS(R) Enterprise Miner(TM)

Target group

Course structure

SAS Enterprise Miner Prediction Fundamentals

• SAS Enterprise Miner prediction setup.
• Prediction basics.
• Constructing a decision tree predictive model.
• Running the regression node.
• Training a neural network.
• Comparing models with summary statistics.

Advanced Methods for Unsupervised Dimension Reduction

• Describe principal component analysis.
• Describe variable clustering.

Advanced Methods for Interval Variable Selection

• Explain how to use partial least squares regression in SAS Enterprise Miner.
• Using LAR/LASSO for variable selection.

Advanced Methods for Nominal Variable Selection and Model Assessment

• Implementing categorical input recoding.
• Creating empirical logit plots.
• Implementing all subsets regression.

Advanced Predictive Models

• Describe the basics of support vector machines.
• Using the HP Forest node in SAS Enterprise Miner to fit a forest model.
• Modeling rare events.
• Using the Rule Induction node in SAS Enterprise Miner.

Multiple Target Prediction

• Appraising model performance.
• Defining a generalized profit matrix.
• Creating generalized assessment plots.
• Using the Two-Stage Model node.
• Constructing component models.

Tips and Tricks with SAS Enterprise Miner

• Using the Open Source Integration node.
• Reusing metadata.
• Importing and using external models (self-study).