Decision trees and tree-based ensembles are supervised learning models used for problems involving classification and regression. This course covers everything from using a single tree to more advanced bagging and boosting ensemble methods in SAS Viya. The course includes discussions of tree-structured predictive models and the methodology for growing, pruning, and assessing decision trees, forest and gradient boosting models. The course also explains isolation forest (an unsupervised learning algorithm for anomaly detection), deep forest (an alternative for neural network deep learning), and Poisson and Tweedy gradient boosted regression trees. In addition, many of the auxiliary uses of trees, such as exploratory data analysis, dimension reduction, and missing value imputation, are examined, and running open source in SAS and running SAS in open source are demonstrated.

The self-study e-learning includes:

• Annotatable course notes in PDF format.
• Virtual lab time to practice.

Learn how to

• Build tree-structured models, including classification trees and regression trees.
• Use the methodology for growing, pruning, and assessing decision trees.
• Build tree-based ensemble models, including forest and gradient boosting.
• Run isolation forest and Poisson and Tweedy gradient boosted regression tree models.
• Provide an introduction to deep forest models.
• Implement open source in SAS and SAS in open source.
• 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 and ensembles of decision trees using SAS Visual Data Mining and Machine Learning in SAS Viya

Formats available	Standard Duration (duration can vary, see event schedule for details)
Classroom:	3 days
Live Web Classroom:	6 half-day session(s)	System Requirements
e-Learning:	21 hours/180 day license

Before attending this course, you should have the following:

• An understanding of basic statistical concepts. You can gain this knowledge from the SAS Visual Statistics in SAS Viya: Interactive Model Building course.
• Familiarity with SAS Visual Data Mining and Machine Learning software. You can gain this knowledge from the Machine Learning Using SAS Viya course.

This course addresses SAS Viya, SAS Visual Data Mining and Machine Learning software.

Introduction to Decision Trees

• Tree-structured models.
• Recursive partitioning.

Growing a Decision Tree

• Split search.
• Splitting criteria.
• Missing values and variable importance.

Preventing Overfitting in Decision Trees

• Pruning.
• Subtree methods.
• Assessing decision trees.

Ensembles of Trees: Bagging, Boosting, and Forest

• Ensembling.
• Bagging.
• Forest models.
• Tree splitting in forests.
• Hyperparameter tuning.
• Model interpretability.

Tree-Based Gradient Boosting Machines

• Boosting.
• Gradient boosting.
• Tree splitting in gradient boosting.
• Early stopping.
• Hyperparameter tuning.
• Model interpretability.

A Practice Case Study

• Data exploration.
• Class levels consolidation.
• Variable selection/dimension reduction.
• Imputation.
• Prediction profiling.