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Hands-On Data Science with SQL Server 2017

You're reading from   Hands-On Data Science with SQL Server 2017 Perform end-to-end data analysis to gain efficient data insight

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781788996341
Length 506 pages
Edition 1st Edition
Languages
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Authors (2):
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Vladimír Mužný Vladimír Mužný
Author Profile Icon Vladimír Mužný
Vladimír Mužný
Marek Chmel Marek Chmel
Author Profile Icon Marek Chmel
Marek Chmel
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Toc

Table of Contents (14) Chapters Close

Preface 1. Data Science Overview FREE CHAPTER 2. SQL Server 2017 as a Data Science Platform 3. Data Sources for Analytics 4. Data Transforming and Cleaning with T-SQL 5. Data Exploration and Statistics with T-SQL 6. Custom Aggregations on SQL Server 7. Data Visualization 8. Data Transformations with Other Tools 9. Predictive Model Training and Evaluation 10. Making Predictions 11. Getting It All Together - A Real-World Example 12. Next Steps with Data Science and SQL 13. Other Books You May Enjoy

Using the PREDICT keyword

SQL Server 2017 introduces the new PREDICT function. This function makes prediction computations much simpler than those that are calculated using R or Python languages, which we looked at in the preceding section. However, the PREDICT function doesn't work with every model that is trained in the arbitrary R (or Python) library.

When the SQL Server started providing machine learning services, new libraries called RevoScaleR for R and RevoScalePy for Python were introduced. These libraries contain their own implementation of several predictive algorithms and also offer the ability to process data in parallel.

Using one of these libraries is a prerequisite when we want to use the PREDICT function. We must also fulfill the following prerequisites:

  • We should use one of following algorithms:
    • rxLinMod
    • rxLogit
    • rxBTrees
    • rxDtree
    • rxForest
    • rxFastTrees
    • rxFastForest...
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