Portugal -- Predictive Modeling Using SAS In-Memory Statistics

4 half-days

Portugal

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.

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.

Who should attend Experienced predictive modelers who need to learn the syntax and functionality of the analytical statements in the IMSTAT procedure Formats available Duration
: 4 half day sessions

Before attending this course, you should have completed . 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.

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

IMPM25.

15 Dezembro
www.sas.com