Random Forests with R

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This book serves as an application-oriented guide to random forests, a statistical learning method known for its excellent predictive performance and flexibility. It can be adapted for both supervised classification and regression problems, accommodating qualitative and quantitative explanatory variables without the need for pre-processing. Random forests excel in scenarios where the number of observations exceeds the number of variables and perform well even in high-dimensional cases. As a result, they have become a favored choice among statisticians and data scientists. The book targets students in statistical education and practitioners in statistics and machine learning, requiring only a scientific undergraduate degree for full comprehension. Minimal computer science knowledge is needed, though familiarity with the R language is recommended. The text begins with an introduction to tree-based methods, followed by a detailed exploration of CART trees and three chapters dedicated to random forests, covering their presentation, variable importance, and variable selection. Each concept is illustrated with a running example, complemented by R code for reproducibility. The book equips readers with essential information, concepts, examples, and software tools necessary for analyzing data using random forests.