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

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...

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$3,000 USD GSA  $2,328.66
Course Code PMA42
Duration 3 days
Available Formats Classroom
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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 Can Benefit

  • Advanced predictive modelers who use SAS Enterprise Miner

Prerequisites

  • 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.

Course Details

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).
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