This Tree-Based Machine Learning Methods in SAS Viya training 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.
By attending Tree-Based Machine Learning Methods in SAS Viya workshop, delegates will learn 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
- An understanding of basic statistical concepts
- Familiarity with SAS Visual Data Mining and Machine Learning software
The Tree-Based Machine Learning Methods in SAS Viya class is ideal for:
- 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