Statistical Learning: Data Mining, Inference, and Prediction

Over the past ten years, there has been a significant growth in computing and information technology, leading to a massive influx of data across various fields such as medicine, biology, finance, and marketing. Understanding this vast amount of data has spurred the development of new statistical tools and the emergence of disciplines like data mining, machine learning, and bioinformatics. Although these tools share common foundations, they are often described using different terminology.

Presented in this book is a unified conceptual framework that encapsulates the fundamental ideas in these areas. While the focus is on concepts rather than complex mathematics, numerous examples are provided, enhanced with colorful graphics. This book will serve as a valuable resource for statisticians and individuals interested in data mining applications in both scientific research and industrial sectors.

Encompassing a wide range of topics, from supervised learning for prediction to unsupervised learning, the book delves into neural networks, support vector machines, classification trees, and boosting - offering the most comprehensive treatment of these subjects. Authors Trevor Hastie, Robert Tibshirani, and Jerome Friedman are distinguished professors of statistics at Stanford University. Hastie and Tibshirani have made significant contributions to the field through their work on generalized additive models and authored a popular book on the topic.

Hastie has also played a key role in developing statistical modeling software in S-PLUS, as well as introducing principal curves and surfaces. Tibshirani is known for his proposal of the Lasso method and is a co-author of the highly successful book, An Introduction to the Bootstrap. Friedman, on the other hand, has co-invented several groundbreaking data-mining tools, including CART, MARS, and projection pursuit.

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