Book Image

Machine Learning with Spark - Second Edition

By : Rajdeep Dua, Manpreet Singh Ghotra
Book Image

Machine Learning with Spark - Second Edition

By: Rajdeep Dua, Manpreet Singh Ghotra

Overview of this book

This book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will start by installing Spark in a single and multinode cluster. Next you'll see how to execute Scala and Python based programs for Spark ML. Then we will take a few datasets and go deeper into clustering, classification, and regression. Toward the end, we will also cover text processing using Spark ML. Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform. You can migrate from Mahout or Scikit to use Spark ML. By the end of this book, you will acquire the skills to leverage Spark's features to create your own scalable machine learning applications and power a modern data-driven business.
Table of Contents (13 chapters)

Training the recommendation model

Once we have extracted these simple features from our raw data, we are ready to proceed with model training; ML takes care of this for us. All we have to do is provide the correctly-parsed input dataset we just created as well as our chosen model parameters.

Split the dataset in to training and testing sets with ratio 80:20, as shown in the following lines of code:

def createALSModel() { 
val ratings = FeatureExtraction.getFeatures();

val Array(training, test) = ratings.randomSplit(Array(0.8, 0.2))
println(training.first())
}

You will see the following output:

16/09/07 13:23:28 INFO Executor: Finished task 0.0 in stage 1.0 (TID 
1). 1768 bytes result sent to driver

16/09/07 13:23:28 INFO TaskSetManager...