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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

Feature preparation


In the previous section, we selected our models and also prepared our dependent variable for our supervised machine learning. In this section, we need to move forward to prepare our independent variables, which are all the features representing the factors impacting our dependent variable: the sales team success. Specifically, for this important work, we need to reduce our four hundred of features to a reasonable group for final modeling. For this, we will employ PCA, utilize some subject knowledge, and then perform some feature selection tasks.

PCA

PCA is a very mature and also commonly used feature reduction method that is often used to find a small set of variables that counts for most of the variance. Technically, the goal of PCA is to find a low dimensional subspace that captures as much of the variance of a dataset as possible.

If you are using MLlib, http://spark.apache.org/docs/latest/mllib-dimensionality-reduction.html#principal-component-analysis-pca has a few...

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