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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

Making the predictive model self-training

One of the common myths about machine learning and predictive models is that they are naturally self-learning, which means that they can re-calculate themselves automatically based on every prediction made. This is not actually true; re-training a predictive model can be very time-consuming. In this section, we'll go through two tips about how to execute regular re-calculation of a predictive model.

Re-calculating a predictive model regularly

In the previous chapter 9, Predictive Model Training and Evaluation, we created a stored procedure called Models.procTrainModel. Let's briefly remind ourselves about what the stored procedure does. It is used to train a movie recommendation...

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