Tree-Based Machine Learning Methods in SAS Viya

Sharad Saxena

ISBN 10: 1954846630 / ISBN 13: 9781954846630

Published by SAS Institute, 2022

About this Item

Tree-Based Machine Learning Methods in SAS Viya | Sharad Saxena | Taschenbuch | Kartoniert / Broschiert | Englisch | 2022 | SAS Institute | EAN 9781954846630 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 121320956

Bibliographic Details

• Title: Tree-Based Machine Learning Methods in SAS Viya
• Publisher: SAS Institute
• Publication Date: 2022
• Language: English
• ISBN 10: 1954846630
• ISBN 13: 9781954846630
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 191H x 20D millimeters 235 millimeters width by 191 millimeters height by 20 millimeters depth
• Weight: 679 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.