Interpreting Classifiers through Attribute Interactions in Datasets

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Henelius, Andreas; Puolamäki, Kai; Ukkonen, Antti;
  • Subject: Statistics - Machine Learning | Computer Science - Learning

In this work we present the novel ASTRID method for investigating which attribute interactions classifiers exploit when making predictions. Attribute interactions in classification tasks mean that two or more attributes together provide stronger evidence for a particula... View more
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  • Related Research Results (1)
    Inferred by OpenAIRE
    astrid-r software on GitHub
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