About this course

Course code TPPMHP123
Duration 3 Days

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 High-Performance procedures in a single-machine mode and in distributed mode

Prerequisites

  • 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

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

Delegates will 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 counterparts in 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.

Outline

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)

3 Days

Duration

This is a QA approved partner course

Delivery Method

Delivery method

Classroom

Face-to-face learning in the comfort of our quality nationwide centres, with free refreshments and Wi-Fi.

Find dates and prices

Online booking is currently not available for this course, to find out more please call us on 0345 074 7998 or email us at info@qa.com to discuss how we can help.

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