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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

DoorDash

Sprinklr

Qualtrics

Ubisoft

Walmart

HubSpot

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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?
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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?.