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- Description:
A thin Python wrapper that uses the javabridge Python library to communicate with a Java Virtual Machine executing Weka API calls. Offers all major APIs, like data generators, loaders, savers, filters, classifiers, clusterers, attribute selection, associations and experiments. Weka packages can be listed/installed/uninstalled as well. It does not provide any graphical frontend, but some basic plotting and graph visualizations are available through matplotlib and pygraphviz.
- Changes to previous version:
- added "create_string" class method to the "Attribute" class for creating a string attribute
- ROC/PRC curves can now consist of multiple plots (ie multiple class labels)
- switched command-line option handling from "getopt" to "argparse"
- fixed Instance.get_dataset(self) method
- added iterators for: rows/attributes in dataset, values in dataset row
- incremental loaders can be iterated now
- BibTeX Entry: Download
- Supported Operating Systems: Agnostic
- Data Formats: Arff, Csv, Libsvm, Xrff
- Tags: Machine Learning, Weka
- Archive: download here
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