Welcome to R for Statistical Learning, your essential companion in navigating the exciting field of machine learning from a statistical perspective, all powered by the versatile R programming language. While our current title highlights R for Statistical Learning, a more descriptive and perhaps more accurate title could indeed be “Machine Learning from a Statistician’s Perspective using R.” However, we believe the current title is more concise and directly reflects our focus: mastering Statistical Learning With R.
About This Book: Your Gateway to Statistical Learning in R
This book originated as a supplementary resource for An Introduction to Statistical Learning (ISL), specifically tailored for the STAT 432 – Basics of Statistical Learning course at the University of Illinois at Urbana-Champaign. Initially conceived to enhance ISL’s introduction to statistical learning using R, primarily by expanding and modifying existing code examples, this text has evolved into a more comprehensive and self-contained guide.
You might ask, why create a separate text when ISL is already considered a leading undergraduate textbook and a key inspiration for STAT 432? This is a valid question. The answer lies in the need for precise control over the learning content to perfectly align with the specific requirements of students enrolled in STAT 432. The primary objective of this book is to directly address the unique needs of these students, which include:
- Extensive R Code Examples and Detailed Explanations: Providing practical, hands-on experience with statistical learning in R through numerous code examples and clear, step-by-step explanations.
- In-depth Simulation Studies: Illustrating theoretical concepts and practical applications of statistical learning with R through comprehensive simulation studies.
- Mathematical Rigor Tailored to the Reader’s Background: Presenting the mathematical foundations of statistical learning in a manner that is accessible and relevant to students with a statistics background.
- Course-Aligned Book Structure: Organizing the content to mirror the structure and flow of the STAT 432 course, ensuring seamless integration with the curriculum.
Essentially, this book aims to capture the best elements of esteemed texts like An Introduction to Statistical Learning, The Elements of Statistical Learning, and Applied Predictive Modeling, focusing on the aspects most crucial and beneficial for our specific student demographic in their journey to master statistical learning with R.
Wordmark of the resource, emphasizing its identity and branding.
Book Organization: A Structured Approach to Statistical Learning
This book is thoughtfully structured into seven key parts to provide a logical and progressive learning experience in statistical learning with R:
- Prerequisites: Outlining the necessary foundational knowledge required to effectively utilize this text, including a review of essential statistical and programming concepts. This section ensures readers are well-prepared to delve into the complexities of statistical learning.
- (Supervised Learning) Regression: Exploring the fundamental concepts and techniques of regression within the framework of supervised learning. This part will cover various regression methods and their implementation in R, crucial for predictive modeling and data analysis.
- (Supervised Learning) Classification: Delving into classification methods, another pillar of supervised learning. Readers will learn to apply different classification algorithms using R to solve real-world problems involving categorical outcomes.
- Unsupervised Learning: Shifting focus to unsupervised learning techniques, this section covers methods for discovering patterns and structures in data without labeled responses. R will be used to implement clustering, dimensionality reduction, and other unsupervised learning approaches.
- (Statistical Learning) in Practice: Bridging the gap between theory and application, this part emphasizes the practical aspects of statistical learning. It focuses on applying the techniques learned in previous parts to real datasets using R, addressing common challenges and best practices.
- (Statistical Learning) in The Modern Era: Introducing advanced and contemporary topics in statistical learning that are widely used in practice today. This section keeps the content relevant and up-to-date with the evolving field of data science and machine learning with R.
- Appendix: Providing supplementary materials, including additional resources, deeper mathematical derivations, and further R code examples to enhance understanding and facilitate further exploration of statistical learning.
Parts 2, 3, and 4 are dedicated to the theoretical foundations of statistical learning. Various methods are introduced to illustrate different theoretical concepts, ensuring a solid understanding of the underlying principles. Parts 5 and 6 then pivot to the practical application of statistical learning. Part 5 focuses on the hands-on usage of techniques covered in the theoretical sections, while Part 6 introduces cutting-edge methods prevalent in contemporary practice, all within the R environment.
Target Audience: Who Should Read This Book?
This book is specifically designed for advanced undergraduate students and first-year Master’s students in Statistics who are new to the field of statistical learning. It is assumed that readers possess prior experience with statistical modeling and R programming. While both of these areas are discussed in detail throughout the text, a foundational understanding is expected to maximize the learning experience and effectively grasp the nuances of statistical learning with R.
Important Notice: Book Under Active Development
Please be aware that this book is a work in progress and is continuously being updated. Much of the initial content was drafted during the Spring 2017 offering of the STAT 432 course. While the combination of this text with ISL provides comprehensive coverage of the subject, significant revisions and expansions are ongoing, particularly during Fall 2017.
To ensure you are accessing the most current version, it is highly recommended to use the online HTML version. The HTML format also offers enhanced features such as adjustable text size, font styles, and color themes for a more personalized reading experience. For those who prefer offline access, a continuously updated PDF version is available. However, please note that during active development, the formatting of the PDF version may not be as refined as the HTML version, primarily due to pagination considerations inherent in PDF formatting.
Given the ongoing development, you may encounter errors, ranging from minor typos to code issues or areas where explanations could be clearer. Your feedback is invaluable in improving this resource! If you identify any errors or areas for improvement, please don’t hesitate to contact us via email (dalpiaz2 AT illinois DOT edu). For those familiar with rmarkdown and GitHub, you are also welcome to submit a pull request to directly contribute fixes. This process is streamlined by the “edit” button located in the top-left corner of the HTML version. If your suggestions or corrections are incorporated into the book, you will be acknowledged in the contributor list at the end of this chapter, with links to your GitHub account or personal website upon request.
Throughout the text, you may find “TODO” notes. These are internal reminders of areas still under development and provide a glimpse into upcoming enhancements. For further details on the development process, please refer to the [README] file on GitHub.
Conventions Used in This Book
0.0.1 Mathematical Notation
This text utilizes MathJax to render mathematical notation for web display. In rare instances, a JavaScript error might prevent MathJax from rendering equations correctly, in which case you will see the underlying code instead of the intended mathematical expressions. Refreshing the page typically resolves this issue. Additionally, by right-clicking on any equation, you can access the MathML code (for use in applications like Microsoft Word) or the TeX command used to generate the equation.
For example, the Pythagorean theorem is rendered as:
[ a^2 + b^2 = c^2 ]
The symbol (triangleq) is frequently used to denote “is defined to be.”
We use (p) to represent the number of predictors and (n) to represent the sample size.
0.0.2 Code Conventions
R code is presented in a monospace font with syntax highlighting for improved readability and understanding.
a <- 3
b <- 4
sqrt(a ^ 2 + b ^ 2)R output lines, as they would appear in the console, are prefixed with ## and generally do not include syntax highlighting.
## [1] 5In terms of coding style, we largely adhere to the tidyverse style guide, with one notable exception. Instead of the conventional assignment operator <-, we opt for the more visually appealing and easier-to-type -. While this is not a widely adopted practice, it is used by a select group of developers and is a stylistic choice in this book.
Acknowledgements
The following is an evolving list of individuals who have contributed to this book:
[Your name could be here!] If you contribute a correction and wish to be acknowledged, please provide your name as you would like it to appear, along with a link to your GitHub, LinkedIn, or personal website. Pull requests are highly encouraged!
Looking for ways to contribute? Consider these areas:
- Code Refactoring: Much of the plotting code is available in the source but not explicitly shown in the text. This code was written to achieve specific tasks but could be refactored for better efficiency and clarity.
- Typo Correction: Given the active development, typos are continuously introduced. Identifying and correcting these is a valuable contribution.
- Suggesting Edits: Providing feedback and suggestions for improving explanations and content is immensely helpful.
License
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. This license allows for sharing and adapting the material for non-commercial purposes, provided appropriate attribution is given and any derivative works are distributed under the same license.
