Classification and regression algorithms for WEKA implemented in Python

dc.contributor.author	Beckham, Christopher J.
dc.date.accessioned	2015-11-23T00:58:36Z
dc.date.available	2015-11-23T00:58:36Z
dc.date.issued	2015-10
dc.identifier.citation	Beckham, C.J. (2015). Classification and regression algorithms for WEKA implemented in Python. (Working paper 02/2015). Hamilton, New Zealand: University of Waikato, Department of Computer Science.	en_NZ
dc.identifier.issn	1177-777X
dc.identifier.uri	https://hdl.handle.net/10289/9773
dc.description.abstract	WEKA is a popular machine learning workbench written in Java that allows users to easily classify, process, and explore data. There are many ways WEKA can be used: through the WEKA Explorer, users can visualise data, train classifiers and examine performance metrics; in the WEKA Experimenter, datasets and algorithms can be compared in an automated fashion; or, it can simply be invoked on the command-line or used as an external library in a Java project.	en_NZ
dc.format.mimetype	application/pdf
dc.language.iso	en	en_NZ
dc.publisher	University of Waikato, Department of Computer Science	en_NZ
dc.relation.ispartofseries	Computer Science Working Papers	en_NZ
dc.rights	© 2015 Christopher J. Beckham	en_NZ
dc.subject	Computer Science	en_NZ
dc.title	Classification and regression algorithms for WEKA implemented in Python	en_NZ
dc.type	Working Paper	en_NZ
uow.relation.series	02/2015	en_NZ