About this course

Course code TPIMPM25
Duration 2 Days

This course focuses on the statistical and machine learning methods for predictive modeling available in the IMSTAT procedure. Topics include building candidate predictive models and assessing predictive models on training and holdout data for honest assessment using the IMSTAT procedure. You learn about methods such as decision trees and random forests using the DECISIONTREE and RANDOMWOODS statements. Modeling a binary response using the LOGISTIC and NEURAL statements is also covered, as is analyzing an interval target with generalized linear models using the GLM and GENMODEL statements. Generating and using Base SAS score code is demonstrated as well. Features of ODS Statistical Graphics are described for visualizing IMSTAT results.

Prerequisites

Before attending this course, you should have completed Getting Started with SAS In-Memory Statistics. Other prerequisites are knowledge of and experience using the analytics methods such as binary logistic regression and decision trees. Understanding of predictive modeling concepts such as honest assessment on holdout data is also required.

This course addresses SAS In-Memory Statistics software.

The software version addressed is SAS LASR Analytics Server 2.5.

Who should attend

Experienced predictive modelers who need to learn the syntax and functionality of the analytical statements in the IMSTAT procedure

Delegates will learn how to

  • distribute SAS tables in the Hadoop Distributed File System (HDFS)
  • load Hadoop tables into LASR memory
  • process in-memory tables with PROC LASR and PROC IMSTAT
  • build predictive models using the PROC IMSTAT statements DECISIONTREE, RANDOMWOODS, LOGISTIC, NEURAL, GLM, and GENMODEL
  • produce assessment statistics using the PROC IMSTAT ASSESS statement
  • produce score code
  • score new data sets
  • generate visual summaries of data using ODS statistical graphics.

Outline

  • Overview of SAS In-Memory Statistics
  • Introduction to the SAS LASR Analytic Server and the IMSTAT Procedure
  • Decision Trees in PROC IMSTAT
  • Logistic Regression in PROC IMSTAT
  • Neural Networks in PROC IMSTAT
  • Modeling Interval Targets in PROC IMSTAT
  • Regression Modeling with SAS Software: Migrating from SAS Foundation Software to SAS In-Memory Statistics

2 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.

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