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

Chapter 7. Extreme Gradient Boosting

By now we should have become quite familiar with machine learning and data science in Java: we have covered both supervised and unsupervised learning and also considered an application of machine learning to textual data. 

In this chapter, we continue with supervised machine learning and will discuss a library which gives state-of-the-art performance in many supervised tasks: XGBoost and Extreme Gradient Boosting. We will look at familiar problems such as predicting whether a URL ranks for the first page or not, performance prediction, and ranking for the search engine, but this time we will use XGBoost to solve the problem.

The outline of this chapter is as follows:

  • Gradient Boosting Machines and XGBoost
  • Installing XGBoost
  • XGBoost for classification
  • XGBoost for regression
  • XGBoost for learning to rank 

By the end of this chapter, you will learn how to build XGBoost from the sources and use it for solving data science problems.