Tree-Based Methods for Statistical Learning in R

Tree-based Methods for Statistical Learning in R provides a comprehensive introduction to individual decision tree algorithms and ensembles, with code-based examples in R and a companion website with supplementary material and code. It covers topics such as CART, conditional inference trees, bagging, boosting, and random forests, and offers practical examples for constructing partial dependence plots and post-processing tree ensembles. The book is designed to be accessible to both R and non-R programmers.

Format: Hardback
Length: 388 pages
Publication date: 23 June 2022
Publisher: Taylor & Francis Ltd

Tree-based Methods for Statistical Learning in R offers a comprehensive and in-depth exploration of both individual decision tree algorithms (Part I) and ensembles thereof (Part II). Part I delves into various tree algorithms, both conventional and contemporary, providing a solid foundation for understanding tree-based ensembles, which are at the forefront of modern statistical and machine learning methodologies.

The book follows a practical and code-oriented approach, emphasizing the use of the R statistical language with minimal reliance on external packages. It guides users through the process of writing their own random forest and gradient tree boosting functions using simple for loops and basic tree fitting software like rpart and party/partykit. Additionally, the core chapters conclude with a detailed section on relevant software in both R and other open-source alternatives, such as Python, Spark, and Julia, along with example usage on real data sets.

While the book primarily utilizes R, its accessibility and usefulness extend to non-R programmers as well. The primary goal is to provide readers with a solid foundation and appreciation for tree-based methods and their applications in solving practical problems and challenges faced by data scientists in applied work.

Key Features:

Thorough Coverage: The book provides comprehensive coverage of tree-based methods, including CART, conditional inference trees, bagging, boosting, and random forests. It covers the fundamental principles, algorithms, and applications of each method, enabling readers to gain a deep understanding of their workings.

A Companion Website: Accompanying the book is a companion website featuring additional supplementary material and the code to reproduce every example and figure in the book. This resource enhances the learning experience and allows readers to delve deeper into the topics covered.

A Companion R Package: A companion R package, named treemisc, is also available. It contains several data sets and functions used throughout the book, facilitating practical implementation and experimentation.

By the end of this book, readers will have gained a solid foundation and appreciation for tree-based methods and their role in solving complex data science problems. Whether you are a seasoned data scientist or a beginner looking to expand your knowledge in statistical learning, Tree-based Methods for Statistical Learning in R is an invaluable resource.

Dimension: 234 x 156 (mm)
ISBN-13: 9780367532468