Book Image

Mastering Java for Data Science

By : Alexey Grigorev
Book Image

Mastering Java for Data Science

By: Alexey Grigorev

Overview of this book

Java is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises. Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort. This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data. Finally, we finish the book by talking about the ways to deploy the model and evaluate it in production settings.
Table of Contents (17 chapters)
Title Page
About the Author
About the Reviewers
Customer Feedback


In machine learning, the classification problems deal with discrete targets with a finite set of possible values. What this means is that there is a set of possible outcomes, and given some features we want to predict the outcome. 

The binary classification is the most common type of classification problem, as the target variable can have only two possible values, such as True/False, Relevant/Not Relevant, Duplicate/Not Duplicate, Cat/Dog, and so on.

Sometimes the target variable can have more than two outcomes, for example, colors, category of an item, model of a car, and so on, and we call this multi-class classification. Typically, each observation can only have one label, but in some settings an observation can be assigned several values. Multi-class classification can be converted to a set of binary classification problems, which is why we will mostly concentrate on binary classification.

Binary classification models

As we have already discussed, the binary classification...