Machine-learning workbench
Weka is an open-source machine-learning workbench from the University of Waikato that aggregates algorithms and utilities for data preparation, modeling, and evaluation. Written in Java, it's for researchers and practitioners to analyze, preprocess data, build predictive models, and evaluate performance. It exposes multiple GUI interfaces, a command-line interface, and a Java API.
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Weka supports supervised and unsupervised learners, association rule mining, time-series tools, and visualization components. Data can be loaded from common text formats such as ARFF and CSV. The project is licensed under the GNU General Public License, and official documentation lives on the Weka Wiki and project homepage.
Build visual pipelines
Weka’s GUIs expose core workflows. Explorer organizes data filtering, attribute selection, training, evaluation, and visual inspection within tabs. The Experimenter compares algorithms using repeatable test runs, while the KnowledgeFlow connects components (data sources, filters, learners) into visual pipelines. All interfaces use the same underlying classes, enabling equivalent tasks. The command line allows scripted execution, and the wiki documents common procedures without custom Java code.
The tool kit ships with a package system that loads add-ons at runtime. Packages bundle compiled classes, sources, metadata, and any required third-party libraries, enabling extension of classifiers, filters, evaluators, and utilities beyond the base distribution. All operations, however, are centered on Java classes and artifacts. The Package Manager then handles the discovery and installation of new components, which appear in the GUIs and command line alongside built-ins.
For programmatic control, the Java API surfaces learners, filters, and evaluators as classes that can be constructed, parameterized, and invoked within development applications. Releases are tagged in source control, and example projects illustrate class usage. The project documents compiling, referencing artifacts, and running diagnostics from the SimpleCLI for troubleshooting (including database/classpath checks). This aligns the GUI, CLI, and API around the same object model, supporting reproducible operations.
Algorithms for every data task
Weka consolidates algorithms, filters, evaluation routines, and visual tools under GUI, CLI, and Java interfaces, with extensions delivered via a package system. It focuses on supervised and unsupervised learning, association rules, time-series utilities, and dataset visualization. However, it relies on Java classes and the boundaries of the packaged algorithm set present at installation time. Documentation centralizes task recipes and versioning references, with release tags indicating stable and development lines.
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