Brandon M. Greenwell (University of Cincinnati, Cincinnati, USA)

Tree-Based Methods for Statistical Learning in R

• Gebundenes Buch

Produktbeschreibung

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.

Produktdetails

• Chapman & Hall/CRC Data Science Series
• Verlag: Taylor & Francis Ltd
• Seitenzahl: 404
• Erscheinungstermin: 23. Juni 2022
• Englisch
• Abmessung: 238mm x 158mm x 31mm
• Gewicht: 782g
• ISBN-13: 9780367532468
• ISBN-10: 0367532468
• Artikelnr.: 63222304

Autorenporträt

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."

Inhaltsangabe

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