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Can you explain how you address the issue of imbalanced datasets, where one class is heavily overrepresented in binary classification?

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  • Can you explain how you address the issue of imbalanced datasets, where one class is heavily overrepresented in binary classification?
  • What approach do you take to handle datasets with imbalanced classes, where one category has a much larger number of instances than the other?
  • In cases where one class is much more prevalent than the other in binary classification, what measures do you put in place to overcome this imbalance?
  • How do you manage datasets where there is a vast difference in the number of instances between the two classes in binary classification?
  • What strategies do you utilize to contend with datasets that exhibit a significant class imbalance in binary classification?
  • What is your preferred method for dealing with datasets that have imbalanced classes, where one category is severely underrepresented compared to the other?
  • How do you deal with binary classification datasets that are unevenly distributed, where one class dominates the other in terms of instance count?
  • Can you elaborate on your decision-making process for handling datasets that display class imbalance issues in binary classification?
  • What steps do you take to address datasets where one class is substantially underrepresented compared to the other in binary classification?
  • What is your typical approach for handling binary classification datasets when one category has significantly more instances than the other?
  • In binary classification, how do you handle imbalanced datasets where one class significantly outnumbers the other?

Interview question asked to Machine Learning Engineers interviewing at Square, Yelp, Asana and others: Can you explain how you address the issue of imbalanced datasets, where one class is heavily overrepresented in binary classification?.