Chapter 10. Faster decision-making with machine learning and PySpark
This chapter covers
- An introduction to machine learning
- Training and applying decision tree classifiers in parallel with PySpark
- Matching problems and appropriate machine learning algorithms
- Training and applying random forest regressors with PySpark
Chapter 9 showed how we can write Python and take advantage of Spark, one of the most popular distributed computing frameworks. We saw some of Spark’s raw data transformation options, and we used Spark in the map and reduce style we’ve been exploring throughout the book. However, one of the reasons why Spark is so popular is its built-in machine learning capabilities.
Machine learning refers to the design, training, application, ...
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