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\u003cp\u003e\u003cb\u003eDiscover how to build decision trees using SAS\u003csup\u003e\u003c/sup\u003e Viya\u003csup\u003e\u003c/sup\u003e!\u003c/p\u003e\u003c/b\u003e\n\u003cp\u003e\u003ci\u003eTree-Based Machine Learning Methods in SAS\u003csup\u003e\u003c/sup\u003e Viya\u003csup\u003e\u003c/sup\u003e\u003c/i\u003e 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.\u003c/p\u003e\n\u003cp\u003eBy the end of this book, you will know how to:\u003cul\u003e\n\u003cli\u003ebuild tree-structured models, including classification trees and regression trees.\n\u003cli\u003ebuild tree-based ensemble models, including forest and gradient boosting.\n\u003cli\u003erun isolation forest and Poisson and Tweedy gradient boosted regression tree models.\n\u003cli\u003eimplement open source in SAS and SAS in open source.\n\u003cli\u003euse decision trees for exploratory data analysis, dimension reduction, and missing value imputation.\u003c/ul\u003e |
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