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

Dimensionality reduction

Dimensionality reduction, as the name suggests, reduces the dimensionality of your dataset. That is, these techniques try to compress the dataset such that only the most useful information is retained, and the rest is discarded.

By dimensionality of a dataset, we mean the number of features of this dataset. When the dimensionality is high, that is, there are too many features, it can be bad due to the following reasons:

  • If there are more features than the items of the dataset, the problem becomes ill-defined and some linear models, such as ordinary least squares (OLS) regression cannot handle this case
  • Some features may be correlated and cause problems with training and interpreting the models
  • Some of the features can turn out to be noisy or irrelevant and confuse the model
  • Distances start to make less sense in high dimensions -- this problem is commonly referred to as the curse of dimensionality
  • Processing a large set of features may be computationally expensive

In the...