Recursive Partitioning And Applications(2nd Edition)

Authors:

Heping Zhang, Burton H. Singer

Recursive Partitioning And Applications2nd EditionHeping Zhang, Burton H. Singer1461426227, 978-1461426226
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ISBN: 1461426227, 978-1461426226

Book publisher: Springer

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Customer Reviews

Trusted feedback from verified buyers

KS
Kaia Sanders
3.0
Reviewed In United States United States • October 2, 2023
If you're looking for a beginner-friendly introduction, this book offers a decent overview but lacks depth in some areas. Could be better with more hands-on examples.
RB
Roger Barrett
5.0
Reviewed In United States United States • September 21, 2023
Recursive Partitioning And Applications, 2nd Edition serves as an excellent resource for both beginner and advanced statisticians. The book's in-depth exploration of statistical methodologies provides clear, practical examples that make complex concepts accessible. I was particularly impressed with the chapter on decision trees, which included real-world applications. Plus, my copy arrived super fast and was well-packaged. Highly recommended for anyone serious about statistical analysis!
DP
Damien Pratt
4.0
Reviewed In United States United States • August 8, 2023
Recursive Partitioning And Applications, 2nd Edition manages to strike a good balance between theory and practical application. The authors excel at breaking down complicated statistical concepts, though, at times, the language can be a bit dense for those not entirely familiar with statistical jargon. My copy arrived quickly and in perfect condition. Great resource for enhancing one's understanding, though not for complete novices.
BS
Bruce Shepherd
5.0
Reviewed In United States United States • July 15, 2023
Got extra discount with my subscription, which was a lovely bonus! This book is truly outstanding when it comes to understanding decision trees and their applications. It digs deep into the theory while providing ample real-world examples. The illustrations and figures were particularly helpful in grasping how to apply the techniques in practice. I found the section on variable importance especially enlightening. A must-have for data scientists!

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