Title
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics),New
Delivery time: 8-12 business days (International)
This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boostingthe first comprehensive treatment of this topic in any book.This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, nonnegative matrix factorisation, and spectral clustering. There is also a chapter on methods for 'wide'' data (p bigger than n), including multiple testing and false discovery rates.
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Shipping & Returns
Shipping
We ship your order within 2–3 business days for USA deliveries and 5–8 business days for international shipments. Once your package has been dispatched from our warehouse, you'll receive an email confirmation with a tracking number, allowing you to track the status of your delivery.
Returns
To facilitate a smooth return process, a Return Authorization (RA) Number is required for all returns. Returns without a valid RA number will be declined and may incur additional fees. You can request an RA number within 15 days of the original delivery date. For more details, please refer to our Return & Refund Policy page.
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Warranty
We provide a 2-year limited warranty, from the date of purchase for all our products.
If you believe you have received a defective product, or are experiencing any problems with your product, please contact us.
This warranty strictly does not cover damages that arose from negligence, misuse, wear and tear, or not in accordance with product instructions (dropping the product, etc.).
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Frequently Asked Questions
- Q: What topics are covered in The Elements of Statistical Learning? A: The book covers a range of topics including supervised learning, unsupervised learning, neural networks, support vector machines, classification trees, boosting, graphical models, random forests, ensemble methods, and methods for wide data.
- Q: Who is the author of this book? A: The author of The Elements of Statistical Learning is Trevor Hastie.
- Q: What is the publication date of the second edition? A: The second edition was published on February 9, 2009.
- Q: What is the binding type of this edition? A: This edition is bound as a hardcover.
- Q: How many pages does the book contain? A: The book contains a total of 767 pages.
- Q: Is this book suitable for beginners in statistical learning? A: Yes, the book emphasizes concepts over mathematics, making it suitable for beginners and those interested in data mining.
- Q: Are there illustrations or graphics included in the book? A: Yes, the book features a liberal use of color graphics to help illustrate the concepts discussed.
- Q: What is the condition of the book? A: The book is in new condition.
- Q: In which category is this book classified? A: The book is classified under the category of Intelligence & Semantics.
- Q: Does this book include advanced topics in statistical learning? A: Yes, the second edition includes many advanced topics not covered in the original edition, such as least angle regression and spectral clustering.