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

Blondel, Mathieu; Pedregosa, Fabian


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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.200504", 
  "title": "Lightning: large-scale linear classification, regression and ranking in Python", 
  "issued": {
    "date-parts": [
      [
        2016, 
        12, 
        13
      ]
    ]
  }, 
  "abstract": "<p>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 http://contrib.scikit-learn.org/lightning/.</p>", 
  "author": [
    {
      "family": "Blondel, Mathieu"
    }, 
    {
      "family": "Pedregosa, Fabian"
    }
  ], 
  "type": "article", 
  "id": "200504"
}
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