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

Course code: PMA42

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.
1 380 EUR

1 670 EUR including VAT

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Course dates

Starting date: Upon request

Type: E-learning

Course duration: 10h 30min

Language: en

Price without VAT: 1 380 EUR


Starting date: Upon request

Type: Upon request

Course duration: 21 hours

Language: en

Price without VAT: 1 800 EUR


Type Course
Language Price without VAT
Upon request E-learning 10h 30min en 1 380 EUR Register
Upon request Upon request 21 hours en 1 800 EUR Register
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Target group

Advanced predictive modelers who use SAS Enterprise Miner

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).


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.
  • Do you need advice or a tailor-made course?


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