Random Forests with R

This book provides an application-oriented guide to random forests, a statistical learning method with excellent predictive performance and flexibility. It is primarily intended for students in academic fields and practitioners in statistics and machine learning, with little background knowledge required. It covers the presentation, variable importance tool, and variable selection problem of random forests and illustrates their implementation on a running example.

\n Format: Paperback / softback
\n Length: 98 pages
\n Publication date: 11 September 2020
\n Publisher: Springer
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This comprehensive book provides an in-depth exploration of random forests, a highly effective statistical learning method widely employed across diverse fields due to its exceptional predictive performance and remarkable flexibility. Random forests excel in both supervised classification tasks and regression problems, accommodating a wide range of data types and structures. What sets them apart is their ability to incorporate qualitative and quantitative explanatory variables simultaneously, eliminating the need for pre-processing. Moreover, these versatile models perform remarkably well even when dealing with high-dimensional data, where the number of variables surpasses the number of observations. Consequently, random forests have become a staple in the toolbox of statisticians and data scientists, offering a robust and reliable solution for a wide array of analytical challenges.

The book is designed with a dual audience in mind: students in academic disciplines such as statistical education and practitioners in statistics and machine learning. While a solid foundation in scientific undergraduate studies is essential to fully grasp the concepts, methods, and tools discussed, individuals with a background in computer science can also benefit from the book. A brief introduction to the R language is recommended as a prerequisite for computer science skills.

The book is structured in a logical and organized manner. After an introductory chapter, Chapter 2 delves into the concept of CART trees, laying the foundation for understanding random forests. The subsequent three chapters are dedicated to random forests, covering their presentation in Chapter 3, the variable importance tool in Chapter 4, and the variable selection problem in Chapter 5. Each chapter is accompanied by illustrative examples and practical implementations, allowing readers to gain a hands-on understanding of the topic. Additionally, the book provides various supplements and resources to further enhance the reader's knowledge and application of random forests.

In conclusion, this book serves as an invaluable resource for anyone seeking to gain a deep understanding of random forests and their applications in statistical learning. Whether you are a student, researcher, or practitioner, this comprehensive guide will provide you with the knowledge and tools necessary to leverage the power of random forests in your work.

\n Weight: 194g\n
Dimension: 157 x 234 x 12 (mm)\n
ISBN-13: 9783030564841\n
Edition number: 2020 ed.\n