The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition
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Select a product to estimate shipping.“The Elements of Statistical Learning” is a comprehensive textbook offering a rigorous introduction to the field of statistical learning. This second edition builds upon the original’s foundational concepts, providing a detailed exploration of data mining, inference, and prediction techniques.
Key Highlights
- Gain a solid understanding of core statistical learning methods.
- Explore a wide range of algorithms for data analysis and modeling.
- Benefit from clear explanations and numerous examples illustrating practical applications.
- Suitable for readers with a background in mathematics and statistics.
Useful Facts
- Publisher: Springer
- Publication Date: February 9, 2009
- Edition: Second Edition 2009
- Language: English
- Pages: 767
- Dimensions: 9.3 x 6 x 1.4 inches
Good Fit For
- Students and researchers in computer science, statistics, and related fields.
- Data scientists and analysts seeking to deepen their knowledge of statistical modeling.
- Anyone interested in learning the theoretical foundations of machine learning.
| Main Specification | |
|---|---|
| Zīmols | by Trevor Hastie (Author) or Robert Tibshirani (Author) or Jerome Friedman (Author) & 0 more Format: Hardcover |
| Publicēšanas datums | 2009 or February 9 |
| Vienības svars | 3,1 mārciņas |
| Product information | |
| Valoda | Angļu |
| Others | |
| Produkta veids | Intelligence & Semantics |
| Izdevums | Second Edition 2009 |
| Izdevējs | Springer |
| Drukas garums | 767 pages |
| ISBN-10 | 0387848576 |
| ISBN-13 | 978-0387848570 |
| Izmēri | 9.3 x 6 x 1.4 inches |
| ISBN10 | 0387848576 |
| ISBN13 | 978-0387848570 |