Book Image

Mastering Scala Machine Learning

By : Alex Kozlov
Book Image

Mastering Scala Machine Learning

By: Alex Kozlov

Overview of this book

Since the advent of object-oriented programming, new technologies related to Big Data are constantly popping up on the market. One such technology is Scala, which is considered to be a successor to Java in the area of Big Data by many, like Java was to C/C++ in the area of distributed programing. This book aims to take your knowledge to next level and help you impart that knowledge to build advanced applications such as social media mining, intelligent news portals, and more. After a quick refresher on functional programming concepts using REPL, you will see some practical examples of setting up the development environment and tinkering with data. We will then explore working with Spark and MLlib using k-means and decision trees. Most of the data that we produce today is unstructured and raw, and you will learn to tackle this type of data with advanced topics such as regression, classification, integration, and working with graph algorithms. Finally, you will discover at how to use Scala to perform complex concept analysis, to monitor model performance, and to build a model repository. By the end of this book, you will have gained expertise in performing Scala machine learning and will be able to build complex machine learning projects using Scala.
Table of Contents (17 chapters)
Mastering Scala Machine Learning
Credits
About the Author
Acknowlegement
www.PacktPub.com
Preface
10
Advanced Model Monitoring
Index

Chapter 8. Integrating Scala with R and Python

While Spark provides MLlib as a library for machine learning, in many practical situations, R or Python present a more familiar and time-tested interface for statistical computations. In particular, R's extensive statistical library includes very popular ANOVA/MANOVA methods of analyzing variance and variable dependencies/independencies, sets of statistical tests, and random number generators that are not currently present in MLlib. The interface from R to Spark is available under SparkR project. Finally, data analysts know Python's NumPy and SciPy linear algebra implementations for their efficiency as well as other time-series, optimization, and signal processing packages. With R/Python integration, all these familiar functionalities can be exposed to Scala/Spark users until the Spark/MLlib interfaces stabilize and the libraries make their way into the new framework while benefiting the users with Spark's ability to execute workflows in a distributed...