How Are Java Libraries Helping Machine Learning?

December 23, 2020
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IT industry has drastically evolved in this decade. In the past few years, Top software engineers have been diligently working towards inventing new tools and techniques that resulted in the introduction of many new technologies. One such field which has been trending for some time is Machine learning.

Machine Learning is a subset field of Artificial Intelligence, it has been a part of the research for a long time but recently many new technologies heavily now rely on it. Virtual personal assistants, online advertising targeting, self-driving vehicles, sentiment analysis, disaster prediction are some of the very prominent applications of Machine Learning and it holds great potential in future as well.

With these applications, a noticeable increase in work in machine learning has been observed. Researchers, Firms and onsite development Companies have been seeking capable Machine Learning programmers which were not that easy to find. Being a fairly new field and quite different from other pre-existing fields, programmers were reluctant in putting their time and effort in learning completely new tools. Even if they wanted to there were not many tools available in the market other than python and R language.

Elite software engineers in the java community came up with the solution by developing new tools and libraries for using Machine learning algorithm to be used in Java. These libraries not only supported existing java coders but also promoted them in experimenting with Machine learning and explore it.

Following are some of prominent Machine Learning libraries for Java technology


Weka is one of the most popular Java machine learning libraries. Weka 3 is an open-source Java-based workbench used for various Machine Learning applications such as data mining, data analysis, and predictive modelling. Weka can be used for applying multiple machine learning algorithms directly to a dataset or called via a Java program. This library features a well-designed GUI along with a command-line interface as well for better control. It is also preferred for developing new Machine Learning schemes.

Massive Online Analysis (MOA)

MOA is another open-source java tool primarily used for machine learning on data streams in real-time. MOA offers a rich collection of machine learning algorithms for regression, classification, outlier detection, clustering, recommender systems, and concept drift detection.


Top developers of Deeplearning4j have been considered one of the most innovative contributors to the Java ecosystem. Deeplearning4j is a commercial-grade, open-source distributed deep-learning library for Java.

It is considered as a DIY tool for Java coders, who wish to apply machine learning algorithms with Hadoop. It is capable of writing programs for pattern recognition and goal-oriented machine learning by recognizing patterns in speech, sound and text formats.


MALLET is a machine learning toolkit for Java. This Java-based library supports several Machine learning applications such as statistical natural language processing, clustering and topic modelling. It supports a wide variety of algorithms like Decision Trees, Naïve Bayes, and Maximum Entropy and code for evaluating classifier performance. MALLET also includes tools for sequence tagging and topic modelling.

Java-ML (Java Machine Learning Library)

Java-ML is another open-source Java framework/Java API mainly targeting data scientists who want to work on java. Its collection of Machine Learning algorithms includes algorithms for data pre-processing, feature selection, classification, and clustering. Although it does not offer a GUI, algorithms of the same type have a very clear common interface which helps java coders for implementation of any new algorithm.

Java-ML has well-documented source code and plenty of code samples and tutorials available making it easy for a java developer to get started.


JSAT or Java Statistical Analysis Tool is also an open-source Machine Learning tool. It offers one of the largest collections of machine learning algorithms. It is built on core Java and does not have any external dependencies. This library was primarily designed for self-education, that is why all of its code is self-contained. It is most suited for any java developer at beginner level and working on a small or medium-sized Machine Learning task.

Encog Machine Learning Framework

Encong is a Java machine learning framework that supports a variety of advanced algorithms including machine learning. It offers a GUI based workbench and also supports classes to normalize and process data.

One of its main offerings includes multi-threaded and scalable training algorithms. It is open-source software and does not require license or activation fees.


Apache Mahout is a well-distributed linear algebra framework. It is written in Java and Scala and is best suited for data scientists, analytics professionals as well as researchers, mathematicians and statisticians. It’s built-in machine learning algorithms makes it a good choice for a java developer new in Machine learning.

Mahout offers a console interface as well as support for Java APIs for algorithms such as clustering, classification, and collaborative filtering. It is one of few java frameworks that are completely business-ready and can handle complex processes with huge size of data.


RepidMiner is another business-ready data science platform built for analysis and machine learning algorithms. The best thing about RapidMiner is it’s set of pre-built features and tools which makes it very convenient for java coders to develop understandable and straightforward machine learning workflows. It’s automated Machine learning functions speed up and simplify their work as well. Being a commercial tool, RapidMiner has an existing big community and extensive documentation.

Apache Spark’s MLib

Apache Spark is a large-scale data processing platform build for Hadoop. MLlib is a Module of Spark which is a scalable machine learning library. Even though MLib is written in Scala, it still made it in this list is because it is perfectly usable in Java along with Python, R, and Scala. MLlib provides support for all commonly used Machine learning algorithms for instance regression, collaborative filtering, classification, clustering, dimensionality reduction, dimensionality and optimization.

A diverse set of libraries:

This list only mentioned a few of the libraries whereas there are many libraries still in development. It clearly reflects the popularity and strong user base of Java technology. Due to Java’s extreme stability, strong community and active contributors, it proves to be a great support for machine learning technology. All these libraries are diverse in terms of implementation, ease of use and features offered and almost all of them offers some unique features.

This range of libraries gives enough liberty to a java developer to go with the one library that perfectly fulfils the requirements of the project he is working on and the nature of the problems he intends to solve.

For instance, JSAT is one of the fastest Java machine learning libraries due to its high performance, flexibility, and the option for a quick start with Machine Learning problems. Whereas Deeplearning4j is known for even better performance by taking advantage of the latest distributed computing frameworks to accelerate training.

If developers are primarily looking for good support and ease of use, MALLET could be their choice as it provides full support for statistical natural language processing and proves to be ideal to be used for analyzing huge collections of text. RapidMiner can also be preferred due to its pre-defined functions for data handling, visualization, and modelling with machine learning algorithms.

See Also: Java Security Vulnerabilities: Case Commentary

On contrary, Weka is known for its vast collection of algorithms and tools for data analysis and predictive modelling. It proves to be a better option for an experienced ML programmer looking for more tools to work upon.

Compared to Weka, Java-ML offers more consistent interfaces. Its extensive set of excellent similarity measures and feature-selection techniques are preferred by many programmers over every other aspect. It also does implementations of some novel ML algorithms that are not present in other java libraries. Java-ML also features Weka bridges to access Weka’s algorithms directly through the Java-ML API providing an option for developers for easy migration from Weka.

Final thoughts:

In this extremely dynamic industry, such remarkable work by some best talent acts as a stepping stone for many aspiring individuals to apply their skills in different fields as well as it also supports new technologies such as Machine learning to reach to its full potential.



Shaharyar Lalani is a developer with a strong interest in business analysis, project management, and UX design. He writes and teaches extensively on themes current in the world of web and app development, especially in Java technology.

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