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PMML in Action (2nd Edition): Unleashing the Power of Open Standards for Data...

by Lin, Wen-Ching; Jena, Tridivesh;... | PB | VeryGood
ThriftBooks
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US $5.61
Approximately£4.13
Condition:
Very Good
May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend ... Read moreAbout condition
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Last updated on 15 May, 2025 01:51:05 BSTView all revisionsView all revisions

Item specifics

Condition
Very Good
A book that has been read and does not look new, but is in excellent condition. No obvious damage to the book cover, with the dust jacket (if applicable) included for hard covers. No missing or damaged pages, no creases or tears, no underlining or highlighting of text, and no writing in the margins. Some identifying marks on the inside cover, but this is minimal. Very little wear and tear. See the seller’s listing for full details and description of any imperfections. See all condition definitionsopens in a new window or tab
Seller notes
“May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend ...
Binding
Paperback
Book Title
PMML in Action (2nd Edition)
Weight
0 lbs
Product Group
Book
IsTextBook
No
ISBN
9781470003241

About this product

Product Identifiers

Publisher
CreateSpace
ISBN-10
1470003244
ISBN-13
9781470003241
eBay Product ID (ePID)
113247953

Product Key Features

Number of Pages
242 Pages
Language
English
Publication Name
PMML in Action (2nd Edition) : Unleashing the Power of Open Standards for Data Mining and Predictive Analytics
Publication Year
2012
Subject
Programming Languages / General
Type
Textbook
Subject Area
Computers
Author
Alex Guazzelli, Tridivesh Jena, Wen-Ching Lin
Format
Trade Paperback

Dimensions

Item Height
0.6 in
Item Weight
15.7 Oz
Item Length
9.2 in
Item Width
6.1 in

Additional Product Features

Intended Audience
Trade
Synopsis
The data mining community has derived a broad foundation of statistical algorithms and software solutions that has allowed predictive analytics to become a standard approach used in science and industry. For many years, much emphasis has been placed on the development of predictive models. As a consequence, the market place offers a range of powerful tools, many open-source, for effective model building. However, once we turn to the operational deployment and practical application of predictive solutions within an existing IT infrastructure, we face a much more limited choice of options. Often it takes months for models to be integrated and deployed via custom code or proprietary processes. The Predictive Model Markup Language (PMML) standard has reached a significant stage of maturity and has obtained broad industry support, allowing users to develop predictive solutions within one application and use another to execute them. Previously, this was very difficult, but with PMML, the exchange of predictive solutions between compliant applications is now straightforward. The aim of this book is to present PMML from a practical perspective. It contains a variety of code snippets so that concepts are made clear through the use of examples. Readers are assumed to have a basic knowledge of predictive analytics and its techniques and so the book is intended for data mining movers and shakers: anyone interested in moving predictive analytic solutions between applications, including students and scientists. PMML in Action is a great way to learn how to represent your predictive solutions through a mature and refined open standard. For the 2nd edition, the book has been completely revised for PMML 4.1, the latest version of PMML. It includes new chapters and an expanded description of how to represent multiple models in PMML, including model ensemble, segmentation, chaining, and composition. The book is divided into six parts, taking you in a PMML journey in which language elements and attributes are used to represent not only modeling techniques but also data pre- and post-processing. With PMML, users benefit from a single and concise standard to represent predictive models, thus avoiding the need for custom code and proprietary solutions. You too can join the PMML movement Unleash the power of predictive analytics and data mining today, The data mining community has derived a broad foundation of statistical algorithms and software solutions that has allowed predictive analytics to become a standard approach used in science and industry. For many years, much emphasis has been placed on the development of predictive models. As a consequence, the market place offers a range of powerful tools, many open-source, for effective model building. However, once we turn to the operational deployment and practical application of predictive solutions within an existing IT infrastructure, we face a much more limited choice of options. Often it takes months for models to be integrated and deployed via custom code or proprietary processes. The Predictive Model Markup Language (PMML) standard has reached a significant stage of maturity and has obtained broad industry support, allowing users to develop predictive solutions within one application and use another to execute them. Previously, this was very difficult, but with PMML, the exchange of predictive solutions between compliant applications is now straightforward. The aim of this book is to present PMML from a practical perspective. It contains a variety of code snippets so that concepts are made clear through the use of examples. Readers are assumed to have a basic knowledge of predictive analytics and its techniques and so the book is intended for data mining movers and shakers: anyone interested in moving predictive analytic solutions between applications, including students and scientists. PMML in Action is a great way to learn how to represent your predictive solutions through a mature and refined open standard. For the 2nd edition, the book has been completely revised for PMML 4.1, the latest version of PMML. It includes new chapters and an expanded description of how to represent multiple models in PMML, including model ensemble, segmentation, chaining, and composition. The book is divided into six parts, taking you in a PMML journey in which language elements and attributes are used to represent not only modeling techniques but also data pre- and post-processing. With PMML, users benefit from a single and concise standard to represent predictive models, thus avoiding the need for custom code and proprietary solutions. You too can join the PMML movement! Unleash the power of predictive analytics and data mining today

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