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, regression problems deal with situations when the label information is continuous. This can be predicting the temperature for tomorrow, the stock price, the salary of a person or the rating of an item on an e-commerce website.

There are many models which can solve the regression problem:

  • Ordinary Least Squares (OLS) is the usual linear regression
  • Ridge regression and LASSO are the regularized variants of OLS
  • Tree-based models such as RandomForest
  • Neural networks

Approaching a regression problem is very similar to approaching a classification problem, and the general framework stays the same:

  • First, you select an evaluation metric
  • Then, you split the data into training and testing
  • You train the model on training, tune parameters using cross-validation, and do the final verification using the held out testing set.

Machine learning libraries for regression

We have already discussed many machine learning libraries that can deal with classification problems. Typically, these...