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Predictive Modeling Using SAS Enterprise Miner 14

Exam Content Guide

Below we provide a list of the objectives that will be tested on the exam.
For more specific details about each objective download the complete exam content guide.

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Data Sources - 20-25%

  • Create data sources from SAS tables in Enterprise Miner
  • Explore and assess data sources
  • Modify source data
  • Prepare data to be submitted to a predictive model

Building Predictive Models - 35-40%

  • Describe key predictive modeling terms and concepts
  • Build predictive models using decision trees
  • Build predictive models using regression
  • Build predictive models using neural networks

Predictive Model Assessment and Implementation - 25-30%

  • Use the correct fit statistic for different prediction types
  • Use decision processing to adjust for oversampling (separate sampling)
  • Use profit/loss information to assess model performance
  • Compare models with the MODEL COMPARISON node
  • Score data sets within Enterprise Miner

Pattern Analysis - 10-15%   (new content)

  • Identify clusters of similar data with the CLUSTER and SEGMENT PROFILE nodes
  • Perform association and sequence analysis (market basket analysis)
Predictive Modeling Using SAS Enterprise Miner 14 Exam

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