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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
US $5.61
ApproximatelyAU $8.72
Condition:
“May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend ”... Read moreabout condition
Very good
A book that does not look new and has been read but is in excellent condition. No obvious damage to the cover, with the dust jacket (if applicable) included for hard covers. No missing or damaged pages, no creases or tears, and no underlining/highlighting of text or writing in the margins. May be very minimal identifying marks on the inside cover. Very minimal wear and tear. See the seller’s listing for full details and description of any imperfections.
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Free Economy Shipping.
Estimated between Mon, 4 Aug and Fri, 8 Aug to 94104
Located in: Aurora, Illinois, United States
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30-day returns. Seller pays for return postage.
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eBay item number:146445441343
Item specifics
- Condition
- Very good
- Seller notes
- 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
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
Item description from the seller
Seller feedback (5,693,692)
- c***m (417)- Feedback left by buyer.Past 6 monthsVerified purchaseWOW!; I cannot believe this 3 Days to Hawaii! ; AAA+++; Excellent Service; Great Pricing; Fast Delivery-Faster Than Expected to Hawaii!; Shipped 05/05, Mon, Received 05/08, Thu to Hawaii using free shipping; USPS Ground Mail, Book in Excellent Condition--Better Than Described ; TLC Packaging; Excellent Seller Communication, Sends updates . Highly Recommended!, Thank you very much!
- v***v (2057)- Feedback left by buyer.Past monthVerified purchaseAlthough this book was not as described, with no picture of it in the listing, the seller communicated well and quickly gave me a full refund while letting me keep it. The minimal packaging left the book a bit vulnerable (typical for ThriftBooks), but it did arrive safely and timely. As usual, it’s hit or miss with this seller, but often times you can get great values, and their customer service is always very good. Many other sellers with millions of transactions don’t even communicate.
- 2***j (113)- Feedback left by buyer.Past monthVerified purchaseI've ordered several items from this vendor now and they've always been what they were advertised as. This was no exception. Both discs were in great shape as well as their jewel cases. Delivery is good, packaging is simple but effective for what you're paying and I haven't had any get damaged. Will keep buying from this vendor because of the results so far and good prices, too.
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