Frontiers in Oncology (Feb 2023)

Common statistical concepts in the supervised Machine Learning arena

  • Hooman H. Rashidi,
  • Hooman H. Rashidi,
  • Samer Albahra,
  • Samer Albahra,
  • Scott Robertson,
  • Scott Robertson,
  • Nam K. Tran,
  • Bo Hu,
  • Bo Hu

DOI
https://doi.org/10.3389/fonc.2023.1130229
Journal volume & issue
Vol. 13

Abstract

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One of the core elements of Machine Learning (ML) is statistics and its embedded foundational rules and without its appropriate integration, ML as we know would not exist. Various aspects of ML platforms are based on statistical rules and most notably the end results of the ML model performance cannot be objectively assessed without appropriate statistical measurements. The scope of statistics within the ML realm is rather broad and cannot be adequately covered in a single review article. Therefore, here we will mainly focus on the common statistical concepts that pertain to supervised ML (i.e. classification and regression) along with their interdependencies and certain limitations.

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