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 Partitioning
binary and multiway splits
splitting criteria
missing valuesPruning
p-value adjustments
profit and loss considerations
cross validation
class probability trees Auxiliary Uses of Trees
data exploration
dimension reduction
imputationEnsembles of Trees
bagging
boosting
gradient boosting
random forests
The hands-on lab is preconfigured to support this course and will not support hands-on practice for all your enrolled courses.
Hands-On Lab Reservation System
When you are planning your study time, keep in mind that the virtual lab takes 30-45 minutes to start
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