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

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

$ 21.07

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

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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