Predictive Modeling Using SAS High-Performance Analytics Procedures

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

Formats available	Duration
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 SAS Enterprise Miner, SAS/STAT, SAS High-Performance Statistics software.

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

• overview of SAS High-Performance Analytics
• overview of SAS High-Performance Analytics procedures
• shared concepts and topics (self-study)

Exploratory Analysis and Descriptive Statistics

• exploratory analysis with the HPCORR, HPDMDB, and HPSUMMARY procedures
• recoding variables with the HPDS2 procedure

Data Preparation and Transformation for Predictive Modeling

• 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

Building Binary Predictive Models

• logistic regression with the HPLOGISTIC procedure
• random forests with the HPFOREST and HP4SCORE procedures
• neural networks with the HPNEURAL procedure

Assessing Binary Predictive Models

• model scoring and assessment procedure

Modeling Interval Targets

• fitting a continuous response with the HPREG procedure
• fitting generalized lienar models with the HPGENSELECT procedure

Appendices

• econometric modeling (self-study)
• nonlinear modeling with SAS High-Performance Analytics (self-study)