Book Image

Learning Apache Spark 2

By : Abbasi
Book Image

Learning Apache Spark 2

By: Abbasi

Overview of this book

Apache Spark has seen an unprecedented growth in terms of its adoption over the last few years, mainly because of its speed, diversity and real-time data processing capabilities. It has quickly become the preferred choice of tool for many Big Data professionals looking to find quick insights from large chunks of data. This book introduces you to the Apache Spark framework, and familiarizes you with all the latest features and capabilities introduced in Spark 2. Starting with a detailed introduction to Spark’s architecture and the installation procedure, this book covers everything you need to know about the Spark framework in the most practical manner. You will learn how to perform the basic ETL activities using Spark, and work with different components of Spark such as Spark SQL, as well as the Dataset and DataFrame APIs for manipulating your data. Then, you will perform machine learning using Spark MLlib, as well as perform streaming analytics and graph processing using the Spark Streaming and GraphX modules respectively. The book also gives special emphasis on deploying your Spark models, and how they can be operated in a clustered mode. During the course of the book, you will come across implementations of different real-world use-cases and examples, giving you the hands-on knowledge you need to use Apache Spark in the best possible manner.
Table of Contents (12 chapters)

ML-tuning - model selection and hyperparameter tuning


Model development is one of the major tasks. However an important ML task is the selection of the best model from among a list of models, and tuning the model for optimal performance. Tuning can obviously be done for the individual steps or the entire pipeline model, which would include multiple algorithms, feature engineering, transformations and selections.

MLLib supports model selection using the following tools:

  • Cross Validator
  • Train Validation Split

We will look at Model Tuning in Chapter 9Building a Recommendation System, on Recommendations to see how we can minimize mean squared error, one of the characteristics of a good model.