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PMML in Action: Unleashing the Power of Open Standards for Data Mining and Predictive Analytics Paperback – May 18, 2010

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Product Details

  • Paperback: 188 pages
  • Publisher: CreateSpace Independent Publishing Platform (May 18, 2010)
  • Language: English
  • ISBN-10: 1452858268
  • ISBN-13: 978-1452858265
  • Product Dimensions: 6.1 x 0.4 x 9.2 inches
  • Shipping Weight: 12.6 ounces
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Best Sellers Rank: #3,221,135 in Books (See Top 100 in Books)

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4 of 4 people found the following review helpful By kalten on June 28, 2010
Format: Paperback Verified Purchase
The book is a good complement to the PMML 4.0 documentation on the [...] website, if your task involves learning as much as possible about a given aspect of PMML. Otherwise, there is not much more than in the publicly available documentation and the information is not made more accessible either.

Do not get confused by the number of pages - the book is printed in something like double spacing and most of the space is occupied by uncommented XML listings.
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1 of 2 people found the following review helpful By Bella on August 11, 2010
Format: Paperback Verified Purchase
I am in the field of predictive analytics but I had never used PMML so I though of buying this book to get familiar with this popular open standard. Overall I liked this book very much. It has a comprehensive review of how a predictive model is structured in PMML, very useful to both users familiar with predictive analytics models in other languages and users new to the field. The book is a great reference for all things PMML and contains lots of useful hands-on PMML code examples that are explained in detail in the text. I like the way it clearly parses the PMML code to deploy a predictive model into elements and describes the attributes that are defined inside each element. I found very useful that the index also lists the language attributes per element. The book is well motivated starting with a nice discussion on how PMML fits on the big picture. I recommend it to scientists or analysts who want to convert existing applications into PMML as well as to new players in the analytics arena that want to experiment with this flexible open deployment language.
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