Springer Series in Statistics Ser.: Elements of Statistical Learning : Data...-image

Springer Series in Statistics Ser.: Elements of Statistical Learning : Data...

$ 21.07

Format: Hardcover Publication Year: 2009 Item Length: 9.4 in Number of Pages: Xxii, 745 Pages Publisher: Springer New York width: 6.5 in Subject: Probability & Statistics / General, Intelligence (Ai) & Semantics, Databases / Data Mining Item Width: 6.5 in Country of Origin: United States Language: English ISBN: 9780387848570 Item Height: 1.5 in Item Weight: 51.2 Oz Publication Name: Elements of Statistical Learning : Data Mining, Inference, and Prediction Type: Textbook height: 1.5 in Series: Springer Series in Statistics Ser. Author: Trevor Hastie, Jerome Friedman, Robert Tibshirani, J. H. Friedman Subject Area: Mathematics, Computers

Description

Springer Series in Statistics Ser.: Elements of Statistical Learning : Data.... The "Elements of Statistical Learning: Data Mining, Inference, and Prediction" is a comprehensive textbook published by Springer New York in 2009. Authored by Trevor Hastie, Jerome Friedman, Robert Tibshirani, and J. H. Friedman, this book covers a range of subjects including probability, statistics, and data mining. With a focus on practical applications, the book offers insights into statistical learning techniques and their use in predicting and modeling real-world data. The hardcover format makes it a durable reference for students and professionals alike, providing a detailed exploration of the mathematics behind modern data analysis. The "Elements of Statistical Learning: Data Mining, Inference, and Prediction" is a comprehensive textbook published by Springer New York in 2009. Authored by Trevor Hastie, Jerome Friedman, Robert Tibshirani, and J. H. Friedman, this book covers a range of subjects including probability, statistics, and data mining. With a focus on practical applications, the book offers insights into statistical learning techniques and their use in predicting and modeling real-world data. The hardcover format makes it a durable reference for students and professionals alike, providing a detailed exploration of the mathematics behind modern data analysis.

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