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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
- Sales Rank: #132863 in Books
- Published on: 2012-01-31
- Original language: English
- Number of items: 1
- Dimensions: 9.21" h x .55" w x 6.14" l, .76 pounds
- Binding: Paperback
- 242 pages
Most helpful customer reviews
5 of 5 people found the following review helpful.
Full of code examples but missing case studies
By Sandro Saitta
Do you know HTML, the common standard for web pages? PMML is the same idea applied to data mining. PMML stands for Predictive Model Markup Language. It is a standard using XML notation to deploy data mining model in the industry. PMML standard comprises data pre-processing and modelling.
The book contains a good (but short) introduction to the standard. Only a few schemas about the various steps of the process are given. Most of the book focuses on the different parts of the standard, illustrated with code snippets. The main objective of this standard is to reduce time to market when deploying data mining solutions.
The book is really useful if you want to deploy your solution using PMML. If you are interested to know more about this standard, this is not the right book (but maybe the only one?). It contains a lot of code, but no case study. This would have been a plus, since most of the material can certainly be found on the web.
4 of 4 people found the following review helpful.
eehhhh...
By Rosiegirl
A very basic but informative book on PMML. It is written in such a way as to require the companion websites at DMG.org and mainly at Zementis.com.
0 of 0 people found the following review helpful.
Five Stars
By JDMiller
Good introducton to PMML
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