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What approaches do you take to address skewed data in model evaluation, and which metrics do you find most useful?

Machine Learning EngineerData Scientist

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  • What approaches do you take to address skewed data in model evaluation, and which metrics do you find most useful?
  • Can you discuss your strategies for managing skewed data during model assessment and the metrics that apply?
  • How do you mitigate the impact of skewed data on model performance, and what metrics do you employ in this context?
  • What is your methodology for dealing with skewed datasets in performance evaluation, and what metrics do you typically use?
  • When faced with skewed data in model evaluation, how do you proceed and what metrics do you consider?
  • How do you adjust for data skewness when assessing the performance of a model, and what are your go-to metrics?
  • Can you explain how you tackle data skew in model performance evaluations and the metrics that are useful in such situations?
  • What techniques do you apply to handle skewed data in model performance analysis, and what metrics are essential in these scenarios?
  • How do you manage the challenge of skewed data when evaluating the effectiveness of a model, and which metrics do you rely on?
  • How do you handle skewed data when evaluating model performance, and what are some common metrics used in such cases?

Interview question asked to Data Scientists and Machine Learning Engineers interviewing at Adobe, Whatsapp, Amgen and others: What approaches do you take to address skewed data in model evaluation, and which metrics do you find most useful?.