Advanced Predictive Modeling Using SAS Enterprise Miner



In this course, you will learn about advanced topics using SAS Enterprise Miner including how to optimize the performance of predictive models beyond the basics. You will also learn about the development of predictive models 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.


Advanced predictive modelers who use Enterprise Miner


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

1. SAS Enterprise Miner Prediction Fundamentals

  • SAS Enterprise Miner prediction setup
  • Prediction basics
  • Decision trees
  • Regression models
  • Neural networks
  • Model comparison

2. Advanced Methods for Dimension Reduction

  • Principal components analysis
  • Partial least squares regression

3. Advanced Methods for Interval Variable Selection

  • Variable clustering

4. Advanced Methods of Variable Selection for Nominal and Interval Variables

  • Empirical logits
  • Smoothed weights of evidence
  • All subsets regression (self-study)

5. Advanced Predictive Models

  • Support vector machines (high performance)
  • Random forest (high performance)
  • Rule induction
  • Incremental response models (self-study)

6. Multiple Target Prediction

  • Generalized profit matrices
  • Basic two-stage models
  • Constructing component models

7. Tips and Tricks

  • Open Source Integration node
  • Reusing metadata
  • Import and use of external models (self-study)