Book Image

Scala and Spark for Big Data Analytics

By : Md. Rezaul Karim, Sridhar Alla
Book Image

Scala and Spark for Big Data Analytics

By: Md. Rezaul Karim, Sridhar Alla

Overview of this book

Scala has been observing wide adoption over the past few years, especially in the field of data science and analytics. Spark, built on Scala, has gained a lot of recognition and is being used widely in productions. Thus, if you want to leverage the power of Scala and Spark to make sense of big data, this book is for you. The first part introduces you to Scala, helping you understand the object-oriented and functional programming concepts needed for Spark application development. It then moves on to Spark to cover the basic abstractions using RDD and DataFrame. This will help you develop scalable and fault-tolerant streaming applications by analyzing structured and unstructured data using SparkSQL, GraphX, and Spark structured streaming. Finally, the book moves on to some advanced topics, such as monitoring, configuration, debugging, testing, and deployment. You will also learn how to develop Spark applications using SparkR and PySpark APIs, interactive data analytics using Zeppelin, and in-memory data processing with Alluxio. By the end of this book, you will have a thorough understanding of Spark, and you will be able to perform full-stack data analytics with a feel that no amount of data is too big.
Table of Contents (19 chapters)

Multinomial classification

In ML, multinomial (also known as multiclass) classification is the task of classifying data objects or instances into more than two classes, that is, having more than two labels or classes. Classifying data objects or instances into two classes is called binary classification. More technically, in multinomial classification, each training instance belongs to one of N different classes subject to N >=2. The goal is then to construct a model that correctly predicts the classes to which the new instances belong. There may be numerous scenarios having multiple categories in which the data points belong. However, if a given point belongs to multiple categories, this problem decomposes trivially into a set of unlinked binary problems, which can be solved naturally using a binary classification algorithm.

Readers are suggested not be confused distinguishing...