Tree-Based Machine Learning Methods in SAS Viya

Discover how to build decision trees using SAS Viya!

Tree-Based Machine Learning Methods in SAS Viya covers everything from using a single tree to more advanced bagging and boosting ensemble methods. The book includes discussions of tree-structured predictive models and the methodology for growing, pruning, and assessing decision trees, forests, and gradient boosted trees. Each chapter introduces a new data concern and then walks you through tweaking the modeling approach, modifying the properties, and changing the hyperparameters, thus building an effective tree-based machine learning model. Along the way, you will gain experience making decision trees, forests, and gradient boosted trees that work for you.

By the end of this book, you will know how to:
• build tree-structured models, including classification trees and regression trees.
• build tree-based ensemble models, including forest and gradient boosting.
• run isolation forest and Poisson and Tweedy gradient boosted regression tree models.
• implement open source in SAS and SAS in open source.
• use decision trees for exploratory data analysis, dimension reduction, and missing value imputation.|

Discover how to build decision trees using SAS Viya!

Tree-Based Machine Learning Methods in SAS Viya covers everything from using a single tree to more advanced bagging and boosting ensemble methods. The book includes discussions of tree-structured predictive models and the methodology for growing, pruning, and assessing decision trees, forests, and gradient boosted trees. Each chapter introduces a new data concern and then walks you through tweaking the modeling approach, modifying the properties, and changing the hyperparameters, thus building an effective tree-based machine learning model. Along the way, you will gain experience making decision trees, forests, and gradient boosted trees that work for you.

By the end of this book, you will know how to:
• build tree-structured models, including classification trees and regression trees.
• build tree-based ensemble models, including forest and gradient boosting.
• run isolation forest and Poisson and Tweedy gradient boosted regression tree models.
• implement open source in SAS and SAS in open source.
• use decision trees for exploratory data analysis, dimension reduction, and missing value imputation.