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dc.contributor.authorBeckham, Christopher J.en_NZ
dc.contributor.authorHall, Mark A.en_NZ
dc.contributor.authorFrank, Eibeen_NZ
dc.date.accessioned2016-08-09T03:07:51Z
dc.date.available2016en_NZ
dc.date.available2016-08-09T03:07:51Z
dc.date.issued2016en_NZ
dc.identifier.citationBeckham, C. J., Hall, M. A., & Frank, E. (2016). WekaPyScript: Classification, regression, and filter schemes for WEKA implemented in Python. Journal of Open Research Software, 4, e33. http://doi.org/10.5334/jors.108en
dc.identifier.urihttps://hdl.handle.net/10289/10585
dc.description.abstractWekaPyScript is a package for the machine learning software WEKA that allows learning algorithms and preprocessing methods for classification and regression to be written in Python, as opposed to WEKA’s implementation language, Java. This opens up WEKA to its machine learning and scientific computing ecosystem. Furthermore, due to Python’s minimalist syntax, learning algorithms and preprocessing methods can be prototyped easily and utilised from within WEKA. WekaPyScript works by running a local Python server using the host’s installation of Python; as a result, any libraries installed in the host installation can be leveraged when writing a script for WekaPyScript. Three example scripts (two learning algorithms and one preprocessing method) are presented.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.rights© 2016 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/
dc.subjectcomputer scienceen_NZ
dc.subjectPythonen_NZ
dc.subjectWEKAen_NZ
dc.subjectdata miningen_NZ
dc.subjectMachine learning
dc.titleWekaPyScript: Classification, regression, and filter schemes for WEKA implemented in Pythonen_NZ
dc.typeJournal Article
dc.identifier.doi10.5334/jors.108en_NZ
dc.relation.isPartOfJournal of Open Research Softwareen_NZ
pubs.begin-pagee33
pubs.elements-id141278
pubs.end-pagee33en_NZ
pubs.volume4en_NZ


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