Preface
Chapter 1 Introduction
Chapter 2 Binary recursive partitioning with CART
Chapter 3 Conditional inference trees
Chapter 4 "The hitchhiker’s GUIDE to modern decision trees"
Chapter 5 Ensemble algorithms
Chapter 6 Peeking inside the “black box”: post-hoc interpretability
Chapter 7 Random forests
Chapter 8 Gradient boosting machines
Bibliography
Index
Biography
Brandon 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.”
"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
