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

XGBoost in practice

After we have successfully built and installed the library, we can use it for creating machine learning models, and in this chapter we will cover three cases: binary classification, regression, and learning to rank models. We will also talk about the familiar use cases: predicting whether a URL is on the first page or search engine results, predicting the performance of a computer, and ranking for our own search engine. 

XGBoost for classification

Now let's finally use it for solving a classification problem! In Chapter 4, Supervised Learning - Classification and Regression, we tried to predict whether a URL is likely to appear on the first page of search results or not. Previously, we created a special object for keeping the features:

public class RankedPage {
    private String url;
    private int position;
    private int page;
    private int titleLength;
    private int bodyContentLength;
    private boolean queryInTitle;
    private int numberOfHeaders;