This course includes discussions of tree-structured predictive models and the methodology for growing, pruning, and assessing decision trees. In addition, this course examines many of the auxiliary uses of trees such as exploratory data analysis, dimension reduction, and missing value imputation.
Learn How To
build tree-structured models, including classification trees and regression trees use the methodology for growing, pruning, and assessing decision trees use decision trees for exploratory data analysis, dimension reduction, and missing value imputation.Who Should Attend
Predictive modelers and data analysts who want to build decision trees using SAS Enterprise Miner software
Prerequisites
Before attending this course, you should;
have an understanding of basic statistical concepts. You can gain this knowledge from the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression course. be familiar with SAS Enterprise Miner software. You can gain this knowledge from the Applied Analytics Using SAS Enterprise Miner course.SAS Products Covered
SAS Enterprise Miner
Course Outline
Tree-Structured Models
classification trees regression treesRecursive Partitioningbinary and multiway splits splitting criteria missing valuesPruningp-value adjustments profit and loss considerationscross validation class probability trees Auxiliary Uses of Treesdata exploration dimension reduction imputationEnsembles of Treesbagging boosting gradient boosting random forests
