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Weka does contain a collection of algorithms and visualization tools meant for predictive modeling and data analysis along with graphical user interfaces and it makes easy access to all these functions. Those students who are keen to know more about Weka decide to study it in details and so, they are also required to complete assignments.

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Getting Familiar to Weka

Weka (Waikato Environment for Knowledge Analysis) is considered a set of machine learning algorithms intended for data mining jobs. It is written in Java and has been developed in New Zealand. You can apply the algorithms of Weka to a dataset directly. Weka comprises tools meant for data pre-processing classification, clustering, regression, visualization, and association rules.

Advantages of Weka

There are many advantages of Weka and that comprises:

  • Weka is freely available under the GNU General Public License.
  • Weka is a complete collection of modeling and data preprocessing processes.
  • Weka is fully portable as it can be applied in the Java programming language. It runs on nearly every modern computing platform.
  • You can easily use Weka because of its superior graphical user interfaces.

Weka does support various standard data mining jobs, more particularly, classification, clustering, data preprocessing, feature selection, and visualization. Every technique of Weka is based on the notion that data is obtainable as a relation or a flat file, and here, each data point gets described through a specific number of features, commonly, normal or numeric attributes.

However, some other attribute kinds too are highly supported. Weka does provide access to SQL databases through the use of Java Database Connectivity. Weka proposes entrée to deep learning and it isn’t capable of multi-relational data mining. However, there is distinct software to transform a collection of supported database tables into one single table which is ideal to process using Weka.

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Features of Weka

  • Open source – Weka is released in the form of open-source software and it happens to be dual licensed. Pentaho Corporation possesses an exclusive license for using the platform meant for business intelligence.
  • Command Line Interface – You can use every feature of Weka from the command line and it can be highly useful to script large jobs.
  • Graphical interface – Weka possesses a GUI (Graphical User Interface) which permits a person to complete his machine learning project minus programming.
  • Documentation – There are several manuals, wikis, manuals, and MOOC courses which can train a person to use the platform of Weka effectually.
  • Java API – Weka is written in Java. Additionally, it proposes an API which happens to be well document and which endorses integration into your very own applications.

Using Weka in Java Code

Weka is acknowledged as a standard Java tool to perform machine learning experiments and to embed trained models in a Java application. You can use it for supervised as well as unsupervised learning. In fact, you can use Weka in three ways, by using a command line, using Weka GUI, and lastly, through its API. If you visit Weka’s library then you will find a huge assortment of machine learning algorithms that are implemented in Java.

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