Interpretable machine learning: explainability in algorithm design

HIGHLIGHTS

  • What: The aim of this paper is to fill this gap and provide a comprehensive survey and analytical study towards AutoML.
  • Who: SOONG CHINGTSING from the Duke University, USA have published the article: Interpretable Machine Learning: Explainability in Algorithm Design, in the Journal: (JOURNAL)

SUMMARY

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LAY DEFINITIONS

  • Algorithm: A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.
  • Machine Learning: A type of ARTIFICIAL INTELLIGENCE that enable COMPUTERS to independently initiate and execute LEARNING when . . .

     

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