Development macOS
Weka for macOS
Prepare datasets, build classification and regression models, and explore clusters with Weka, an open-source machine learning workbench. This Mac download is for Apple Silicon.
- Operating system
- macOS; minimum version not established by the cited download page
- Architecture
- 64-bit ARM (Apple Silicon)
- Download package
- DMG disk image containing a Mac application
Includes Bellsoft OpenJDK 25 for ARM Macs. A separate Intel Mac disk image is available from the publisher.
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What is Weka?
Weka brings data preparation and machine learning algorithms into a Java workbench. Use it to transform a dataset, train classification or regression models, evaluate results with cross-validation, or group examples with clustering algorithms. Its graphical tools support interactive exploration, while its command-line interface suits repeatable workflows. Additional learning schemes and tools can be added through the package manager.
This entry links to the Weka 3.8.7 disk image for ARM-based Macs, including Bellsoft's 64-bit OpenJDK 25 runtime. The publisher offers a separate Intel Mac disk image; choose that package instead if your Mac has an Intel processor.
Dataset size is a practical constraint: most Weka schemes need the data in memory. CSV import is supported, but separate training and test CSV files can acquire incompatible nominal-attribute definitions. Weka's ARFF format preserves explicit attribute information. The screenshot shows the publisher's historical Mac package-manager interface, not a verified capture of version 3.8.7.
What it offers
- Transform datasets with preprocessing filters for attributes and instances.
- Build classification and regression models and evaluate them with cross-validation.
- Explore clustering and visualize results such as errors, clusters and trees where supported by the selected scheme.
- Add learning algorithms and tools through graphical or command-line package management.
- Save trained models and reuse them to make predictions on compatible data.
Good to know
- Most learning schemes require the dataset in memory, which can limit work with large datasets.
- Separate training and test CSV files may infer incompatible nominal attributes; check their structure or use explicit ARFF definitions.
- The linked disk image targets ARM-based Macs; Intel Macs need the publisher's separate Intel package.
Product images
Sources
References used for this listing. Retrieval dates do not indicate hands-on testing.
- Downloading and installing Weka - Weka Wiki ↗Retrieved
- Primer - Weka Wiki ↗Retrieved
- Manager - Weka Wiki ↗Retrieved
- Commercial applications - Weka Wiki ↗Retrieved
- I cannot process large datasets any ideas - Weka Wiki ↗Retrieved
- Use csv files - Weka Wiki ↗Retrieved
- Visualization - Weka Wiki ↗Retrieved
- How do i make predictions with a trained model - Weka Wiki ↗Retrieved
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