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Advanced Predictive Modeling Using SAS Enterprise Miner
This course covers advanced topics using SAS Enterprise Miner including how to optimize the performance of predictive models beyond the basics.
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About Program
Format of Training
Course Curriculum
Course Fees
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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.
Format of Training
Taught by certified instructors at high-tech facilities across the country
- A SAS expert at your side.
- Focused learning away from the office
- Networking opportunities
- State-of-the-art facilities
- Electronic course notes downloadable to your device and permission to print
- Business Knowledge Series: in-depth courses on the latest business topics
- We offer Connected Classes! Watch for courses in Cary, New York, Arlington, Dallas and San Francisco that connect remote students via our Live Web classroom.
All the benefits of the classroom without the travel
- Join the classroom right from your desktop
- Led by an expert instructor who can virtually look over your shoulder
- Ask questions and get answers in real-time
- Access the latest software via a virtual lab
- Receive 20 business days' access to a recording of your course
- Discuss, share, exchange ideas with participants from different countries
Course Curriculum
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
Advanced Methods for Unsupervised Dimension Reduction
- describe principal components analysis
- describe variable clustering
Advanced Methods for Interval Variable Selection
- explain how to use partial least squares regression in SAS Enterprise Miner
- use 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
- use the HP Forest node in SAS Enterprise Miner to fit a forest model
- modeling rare events
- use 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 use of external models (self-study)
Course Fees
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