ML pipelines for real-life machine learning applications

This is the first of two recipes which cover the ML pipeline in Spark 2.0. For a more advanced treatment of ML pipelines with additional details such as API calls and parameter extraction, see later chapters in this book.

In this recipe, we attempt to have a single pipeline that can tokenize text, use HashingTF (an old trick) to map term frequencies, run a regression to fit a model, and then predict which group a new term belongs to (for example, news filtering, gesture classification, and so on).

How to do it...

1. Start a new project in IntelliJ or in an IDE of your choice. Make sure that the necessary JAR files are included.

2. Set up the package location where the program will reside:

package spark.ml.cookbook.chapter4

3. Import the necessary packages for the Spark session to gain access to the cluster and log4j.Logger to reduce the amount of output produced by Spark:

 

import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification...