This course introduces the functionality in the SAS High-Performance Statistics and Data Mining procedures for predictive modeling. The course shows examples of working with SAS High-Performance procedures in a single-machine mode and in distributed mode.
Learn how to
- set session options to specify the high-performance architecture for a SAS session
- explain how High-Performance procedures are designed and intended to be used
- identify similarities and differences between traditional SAS procedures and their SAS High-Performance Analytics counterparts
- use SAS High-Performance procedures to build and assess predictive models for a binary target
- analyze an interval target using SAS High-Performance procedures
- perform model selection for generalized linear models
- fit zero-inflated models with variable selection.
Who should attend
Experienced statisticians and predictive modelers who need to learn the functionality and use of SAS High-Performance Analytics procedures to build and assess predictive models
| Classroom:|| 3.0 days |
Before attending this course, you should have
- experience in statistical analysis and predictive modeling using SAS/STAT
- experience using SAS programming.
It is recommended that you have previously completed the Predictive Modeling Using Logistic Regression course or have equivalent knowledge and experience.
This course addresses High-Performance procedures that are shipped with SAS/STAT (12.3) and procedures that are licensed with SAS Enterprise Miner (12.3), and touches on SAS/ETS software.
Introduction to SAS High-Performance Analytics
Exploratory Analysis and Descriptive Statistics
- overview of SAS High-Performance Analytics
- overview of SAS High-Performance Analytics procedures
- shared concepts and topics (self-study)
Data Preparation and Transformation for Predictive Modeling
- exploratory analysis with the HPCORR, HPDMDB, and HPSUMMARY procedures
- recoding variables with the HPDS2 procedure
Building Binary Predictive Models
- partitioning data with the HPSAMPLE procedure
- imputing missing values with the HPIMPUTE procedure
- creating new inputs with the HPBIN procedure
- selecting variables using the HPREDUCE procedure
Assessing Binary Predictive Models
- logistic regression with the HPLOGISTIC procedure
- random forests with the HPFOREST and HP4SCORE procedures
- neural networks with the HPNEURAL procedure
Modeling Interval Targets
- model scoring and assessment procedure
- fitting a continuous response with the HPREG procedure
- fitting generalized lienar models with the HPGENSELECT procedure
- econometric modeling (self-study)
- nonlinear modeling with SAS High-Performance Analytics (self-study)