iMerit (2)

• The Three Edge Case Culprits: Bias, Variance, and Unpredictability - Apr 22, 2021.
  
  Edge cases occur for three basic reasons: Bias – the ML system is too ‘simple’; Variance – the ML system is too ‘inexperienced’; Unpredictability – the ML system operates in an environment full of surprises. How do we recognize these edge cases situations, and what can we do about them?

  Bias, iMerit, Machine Learning, Variance

• How Noisy Labels Impact Machine Learning Models - Apr 6, 2021.
  
  Not all training data labeling errors have the same impact on the performance of the Machine Learning system. The structure of the labeling errors make a difference. Read iMerit’s latest blog to learn how to minimize the impact of labeling errors.

  Data Labeling, Data Preparation, iMerit, Machine Learning