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Lightning: large-scale linear classification, regression and ranking in Python

Blondel, Mathieu; Pedregosa, Fabian

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  <identifier identifierType="DOI">10.5281/zenodo.200504</identifier>
      <creatorName>Blondel, Mathieu</creatorName>
      <creatorName>Pedregosa, Fabian</creatorName>
    <title>Lightning: large-scale linear classification, regression and ranking in Python</title>
    <subject>machine learning</subject>
    <subject>supervised learning</subject>
    <date dateType="Issued">2016-12-13</date>
  <resourceType resourceTypeGeneral="Software"/>
    <alternateIdentifier alternateIdentifierType="url"></alternateIdentifier>
    <rights rightsURI="">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;Lightning is a Python library for large-scale machine learning. More specifically, the library focuses on linear models for classification, regression and ranking. Lightning is the first project to integrate scikit-learn-contrib, a repository of high-quality projects that follow the same API conventions as scikit-learn. Compared to scikit-learn, the main advantages of lightning are its scalability and its flexibility. Indeed, lightning implements cutting-edge optimization algorithms that allow to train models with millions of samples within seconds on commodity hardware. Furthermore, lightning can leverage prior knowledge thanks to so-called structured penalties, an area of research that has recently found applications in domains as diverse as biology, neuroimaging, finance or text processing. Lightning is available under the 3-clause BSD license at;/p&gt;</description>
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