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Which techniques did you apply to address multicollinearity in your data?

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  • Which techniques did you apply to address multicollinearity in your data?
  • How did you mitigate multicollinearity, and what was your threshold for VIF?
  • What methods have you employed to tackle multicollinearity in your models?
  • Could you discuss how you resolved multicollinearity issues?
  • How do you handle multicollinearity and what VIF values do you consider acceptable?
  • What strategies did you use to eliminate multicollinearity and what VIF cut-off did you set?
  • In what ways have you dealt with multicollinearity in regression analysis?
  • What was your approach to removing multicollinearity from your predictive models?
  • How do you identify and rectify multicollinearity in your datasets?
  • Explain your process for dealing with multicollinearity and your choice of VIF values.
  • What did you use to remove multicollinearity? Explain what values of VIF you used?

Interview question asked to Machine Learning Engineers interviewing at Dell, Benchling, Audible and others: Which techniques did you apply to address multicollinearity in your data?.