Acknowledgments
Introduction
PART I: PROLOGUE, AND NEIGHBORHOOD-BASED METHODS
Chapter 1: Regression Models
Chapter 2: Classification Models
Chapter 3: Bias, Variance, Overfitting, and Cross-Validation
Chapter 4: Dealing with Large Numbers of Features
PART II: TREE-BASED METHODS
Chapter 5: A Step Beyond k-NN: Decision Trees
Chapter 6: Tweaking the Trees: Bagging, Random Forests, and Boosting
Chapter 7: Finding a Good Set of Hyperparameters
PART III: METHODS BASED ON LINEAR RELATIONSHIPS
Chapter 8: Parametric Methods: Linear and Generalized Linear Models
Chapter 9: Cutting Things Down to Size: Regularization
PART IV: METHODS BASED ON SEPARATING LINES AND PLANES
Chapter 10: A Boundary Approach: Support Vector Machines
Chapter 11: Linear Models on Steroids: Neural Networks
PART V: APPLICATIONS
Chapter 12: Image Classification
Chapter 13: Handling Time Series and Text Data
Appendix A: List of Acronyms and Symbols
Appendix B: Statistics–Machine Learning Terminology Correspondence
Appendix C: Matrices, Data Frames, and Factor Conversions
Appendix D: Pitfall: Beware of “p-Hacking”!
The chapters in red are included in this Early Access PDF.
The Art of Machine Learning
Look Inside!
Download Chapter 6: TWEAKING THE TREES
Machine learning without advanced math! This book presents a serious, practical look at machine learning, preparing you for valuable insights on your own data. The Art of Machine Learning is packed with real dataset examples and sophisticated advice on how to make full use of powerful machine learning methods. Readers will need only an intuitive grasp of charts, graphs, and the slope of a line, as well as familiarity with the R programming language. You’ll become skilled in a range of machine learning methods, starting with the simple k-Nearest Neighbors method (k-NN), then on to random forests, gradient boosting, linear/logistic models, support vector machines, the LASSO, and neural networks. Final chapters introduce text and image classification, as well as time series. You’ll learn not only how to use machine learning methods, but also why these methods work, providing the strong foundational background you’ll need in practice. Additional features:
- How to avoid common problems, such as dealing with “dirty” data and factor variables with large numbers of levels
- A look at typical misconceptions, such as dealing with unbalanced data
- Exploration of the famous Bias-Variance Tradeoff, central to machine learning, and how it plays out in practice for each machine learning method
- Dozens of illustrative examples involving real datasets of varying size and field of application
- Standard R packages are used throughout, with a simple wrapper interface to provide convenient access.
After finishing this book, you will be well equipped to start applying machine learning techniques to your own datasets.