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  • Book Overview & Buying Apache Spark Machine Learning Blueprints
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Apache Spark Machine Learning Blueprints

Apache Spark Machine Learning Blueprints

By : Alex Liu
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Apache Spark Machine Learning Blueprints

Apache Spark Machine Learning Blueprints

1 (1)
By: Alex Liu

Overview of this book

There's a reason why Apache Spark has become one of the most popular tools in Machine Learning – its ability to handle huge datasets at an impressive speed means you can be much more responsive to the data at your disposal. This book shows you Spark at its very best, demonstrating how to connect it with R and unlock maximum value not only from the tool but also from your data. Packed with a range of project "blueprints" that demonstrate some of the most interesting challenges that Spark can help you tackle, you'll find out how to use Spark notebooks and access, clean, and join different datasets before putting your knowledge into practice with some real-world projects, in which you will see how Spark Machine Learning can help you with everything from fraud detection to analyzing customer attrition. You'll also find out how to build a recommendation engine using Spark's parallel computing powers.
Table of Contents (13 chapters)
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12
Index

Results explanation


As before, once we pass the model evaluation stage and select the estimated model as our final model, the next task is to interpret the results for the company executives and technicians.

In the next section, we will work on results explanation focusing on some big influencing variables. With the big influencing variables identified, the company could use them to improve their marketing effort to recruit the right customers.

Big influencers and their impacts

With logistic regression results, we can explain the impact of each feature by using regression coefficients, and identify big influencers by comparing those coefficients.

With the same logic, we can also rank each feature by its effects as calculated by the logistic regression coefficients.

Another way is to use the R package of effect, which was created by John Fox and others especially for the display of the effects of linear and generalized linear models. By using this package, we can obtain a list and some graphical...

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