This course covers advanced topics using SAS Enterprise Miner, including how to optimize the performance of predictive models beyond the basics. The course continues the development of predictive models that begins in the Applied Analytics Using SAS Enterprise Miner course, for example, by making use of the two-stage modeling node. In addition, some of the newest modeling nodes and latest variable selection methods are covered. Tips for working in an efficient way with SAS Enterprise Miner complete the course.

The self-study e-learning includes:

• Annotatable course notes in PDF format.
• Virtual lab time to practice.

Who should attend

Formats available	Duration
Classroom:	3 days
Live Web Classroom:	6 half-day session(s)	System Requirements
e-Learning:	30 hours/180 day license	System Requirements

Before attending this course, it is recommended that you:

• Have completed the Applied Analytics Using SAS Enterprise Miner course.
• Have some experience with creating and managing SAS data sets, which you can gain from the SAS Programming 1: Essentials course.
• Have some experience building statistical models using SAS/STAT software.
• Have completed a statistics course that covers linear regression and logistic regression.

This course addresses SAS Enterprise Miner software.

• 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.

• Describe principal component analysis.
• Describe variable clustering.

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

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

• 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.

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

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