Tree-Based Methods for Statistical Learning in R

ISBN-13: 9780367532468 / Angielski / Twarda / 2022 / 400 str.

Brandon M. Greenwell

Opis

This book provides a thorough introduction to both individual decision tree algorithms (Part I) and ensembles thereof (Part II). Part I of the book brings several different tree algorithms into focus, both conventional and contemporary. 

Spis treści

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

O autorze

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.”