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OverviewFull Product DetailsAuthor: Brandon M. Greenwell (University of Cincinnati, Cincinnati, USA)Publisher: Taylor & Francis Ltd Imprint: Chapman & Hall/CRC Weight: 0.740kg ISBN: 9780367532468ISBN 10: 0367532468 Pages: 404 Publication Date: 23 June 2022 Audience: College/higher education , General/trade , Tertiary & Higher Education , General Format: Hardback Publisher's Status: Active Availability: In Print  This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of Contents"1 Introduction 2 Binary recursive partitioning with CART 3 Conditional inference trees 4 ""The hitchhiker’s GUIDE to modern decision trees"" 5 Ensemble algorithms 6 Peeking inside the “black box”: post-hoc interpretability 7 Random forests 8 Gradient boosting machines"Reviews"Tree-based algorithms have been a workhorse for data science teams for decades, but the data science field has lacked an all-encompassing review of trees - and their modern variants like XGBoost - until now. Greenwell has written the ultimate guide for tree-based methods: how they work, their pitfalls, and alternative solutions. He puts it all together in a readable and immediately usable book. You're guaranteed to learn new tips and tricks to help your data science team. -Alex Gutman, Director of Data Science, Author: Becoming a Data Head ""Here’s a new title that is a “must have” for any data scientist who uses the R language. It’s a wonderful learning resource for tree-based techniques in statistical learning, one that’s become my go-to text when I find the need to do a deep dive into various ML topic areas for my work."" Daniel D. Gutierrez, Editor-in-Chief for insideBIGDATA, USA, insideBIGDATA, February 2023" Tree-based algorithms have been a workhorse for data science teams for decades, but the data science field has lacked an all-encompassing review of trees - and their modern variants like XGBoost - until now. Greenwell has written the ultimate guide for tree-based methods: how they work, their pitfalls, and alternative solutions. He puts it all together in a readable and immediately usable book. You're guaranteed to learn new tips and tricks to help your data science team. -Alex Gutman, Director of Data Science, Author: Becoming a Data Head Author InformationBrandon M. Greenwell is a data scientist at 84.51° where he works on a diverse team to enable, empower, and enculturate statistical and machine learning best practices where it’s applicable to help others solve real business problems. He received a B.S. in Statistics and an M.S. in Applied Statistics from Wright State University, and a Ph.D. in Applied Mathematics from the Air Force Institute of Technology. He's currently part of the Adjunct Graduate Faculty at Wright State University, an Adjunct Instructor at the University of Cincinnati, the lead developer and maintainer of several R packages available on CRAN (and off CRAN), and co-author of “Hands-On Machine Learning with R.” Tab Content 6Author Website:Countries AvailableAll regions |
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