Book Image

Mastering Java Machine Learning

By : Uday Kamath, Krishna Choppella
Book Image

Mastering Java Machine Learning

By: Uday Kamath, Krishna Choppella

Overview of this book

Java is one of the main languages used by practicing data scientists; much of the Hadoop ecosystem is Java-based, and it is certainly the language that most production systems in Data Science are written in. If you know Java, Mastering Machine Learning with Java is your next step on the path to becoming an advanced practitioner in Data Science. This book aims to introduce you to an array of advanced techniques in machine learning, including classification, clustering, anomaly detection, stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, deep learning, and big data batch and stream machine learning. Accompanying each chapter are illustrative examples and real-world case studies that show how to apply the newly learned techniques using sound methodologies and the best Java-based tools available today. On completing this book, you will have an understanding of the tools and techniques for building powerful machine learning models to solve data science problems in just about any domain.
Table of Contents (20 chapters)
Mastering Java Machine Learning
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Linear Algebra
Index

Case study in active learning


This case study uses another well-known publicly available dataset to demonstrate active learning techniques using open source Java libraries. As before, we begin with defining the business problem, what tools and frameworks are used, how the principles of machine learning are realized in the solution, and what the data analysis steps reveal. Next, we describe the experiments that were conducted, evaluate the performance of the various models, and provide an analysis of the results.

Tools and software

For the experiments in Active Learning, JCLAL was the tool used. JCLAL is a Java framework for Active Learning, supporting single-label and multi-label learning.

Note

JCLAL is open source and is distributed under the GNU general public license: https://sourceforge.net/p/jclal/git/ci/master/tree/.

Business problem

The abalone dataset, which is used in these experiments, contains data on various physical and anatomical characteristics of abalone—commonly known as sea...