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SUMMARY:Dolores Romero Morales (Copenhagen Business School)
DTSTART:20200616T183000Z
DTEND:20200616T190000Z
DTSTAMP:20260423T021859Z
UID:DOTs/14
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/DOTs/14/">On
  Enhancing the Interpretability of Data Science Models via Dimensionality 
 Reduction</a>\nby Dolores Romero Morales (Copenhagen Business School) as p
 art of Discrete Optimization Talks\n\n\nAbstract\nData Science aims to dev
 elop models that extract knowledge from complex data to aid Data Driven De
 cision Making. There is a growing literature on enhancing the interpretabi
 lity of Data Science methods. Interpretability is desirable for non-expert
 s\; it is required by regulators for models aiding\, for instance\, credit
  scoring\; and since 2018 the EU has extended this requirement by imposing
  the so-called right-to-explanation. Mathematical Optimization has shown a
  crucial role when striking a balance between interpretability and accurac
 y\, having LASSO as one of the main exponents. In this presentation\, we w
 ill navigate through some novel dimensionality reduction techniques embedd
 ed in the construction of data science models\, to enhance their interpret
 ability.\n
LOCATION:https://researchseminars.org/talk/DOTs/14/
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