Weka contains a collection of visualization tools and algorithms for data analysis and predictive modeling, together with graphical user interfaces for easy access to these functions. The original non-Java version of Weka was a Tcl/Tk front-end to modeling algorithms implemented in other programming languages, plus data preprocessing utilities in C, and a Makefile-based system for running machine learning experiments. This original version was primarily designed as a tool for analyzing data from agricultural domains, but the more recent fully Java-based version, for which development started in 1997, is now used in many different application areas, in particular for educational purposes and research. Advantages of Weka include:
Weka supports several standard data mining tasks, more specifically, data preprocessing, clustering, classification, regression, visualization, and feature selection. All of Weka's techniques are predicated on the assumption that the data is available as one flat file or relation, where each data point is described by a fixed number of attributes. Weka provides access to SQL databases using Java Database Connectivity and can process the result returned by a database query. Weka provides access to deep learning with Deeplearning4j. It is not capable of multi-relational data mining, but there is separate software for converting a collection of linked database tables into a single table that is suitable for processing using Weka. Another important area that is currently not covered by the algorithms included in the Weka distribution is sequence modeling.
User interfaces
Weka's main user interface is the Explorer, but essentially the same functionality can be accessed through the component-based Knowledge Flow interface and from the command line. There is also the Experimenter, which allows the systematic comparison of the predictive performance of Weka's machine learning algorithms on a collection of datasets. The Explorer interface features several panels providing access to the main components of the workbench:
The Preprocess panel has facilities for importing data from a database, a comma-separated values file, etc., and for preprocessing this data using a so-called filtering algorithm. These filters can be used to transform the data and make it possible to delete instances and attributes according to specific criteria.
The Classify panel enables applying classification and regression algorithms to the resulting dataset, to estimate the accuracy of the resulting predictive model, and to visualize erroneous predictions, receiver operating characteristic curves, etc., or the model itself.
The Associate panel provides access to association rule learners that attempt to identify all important interrelationships between attributes in the data.
The Cluster panel gives access to the clustering techniques in Weka, e.g., the simple k-means algorithm. There is also an implementation of the expectation maximization algorithm for learning a mixture of normal distributions.
The Select attributes panel provides algorithms for identifying the most predictive attributes in a dataset.
The Visualize panel shows a scatter plot matrix, where individual scatter plots can be selected and enlarged, and analyzed further using various selection operators.
Native regression tools
Weka has a large number of regression and classification tools. Native packages are the ones included in the executable Weka software, while other non-native ones can be downloaded and used within R.Weka environment. Among the native packages, the most famous tool is the M5p model tree package. The full list of tools is available . Some of the regression tools are:
M5Rules
DecisionStump
M5P
RandomForest
REPTree
ZeroR
DecisionRules
LinearRegression
SMOreg
SimpleLinearRegression
MultiLayerPerceptron
GaussianProcesses
Extension packages
In version 3.7.2, a package manager was added to allow the easier installation of extension packages. Some functionality that used to be included with Weka prior to this version has since been moved into such extension packages, but this change also makes it easier for others to contribute extensions to Weka and to maintain the software, as this modular architecture allows independent updates of the Weka core and individual extensions.
History
In 1993, the University of Waikato in New Zealand began development of the original version of Weka, which became a mix of Tcl/Tk, C, and Makefiles.
In 1997, the decision was made to redevelop Weka from scratch in Java, including implementations of modeling algorithms.
In 2006, Pentaho Corporation acquired an exclusive licence to use Weka for business intelligence. It forms the data mining and predictive analytics component of the Pentaho business intelligence suite. Pentaho has since been acquired by Hitachi Vantara, and Weka now underpins the PMI open source component.
