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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

Explanations of the results


As before, after we have passed our model evaluation stage and decided to select the estimated and evaluated model as our final model, our next task is to interpret the results for the city management and technicians.

In terms of explaining the machine learning results, the city is particularly interested in understanding what factors influence the service request number and how service requests change over time.

So, to serve the city governments and other interested civic organizations, we need to set our focus on further deriving results about big influencing variables and time series trends with our final models. Then, we need to work on interpretations as well as some visualizations as R provides many good visualization solutions.

Biggest influencers

In terms of finding out the features with the largest impact on the target feature, as you learned from the previous chapters, the random forest method is a good solution. Therefore, once our Zeppelin notebook is...

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