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In binary classification, how do you handle imbalanced datasets where one class significantly outnumbers the other?

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Microsoft

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Ironclad

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Yandex

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In binary classification, how do you handle imbalanced datasets where one class significantly outnumbers the other?
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?
Can you explain how you address the issue of imbalanced datasets, where one class is heavily overrepresented in binary classification?

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Interview question asked to Data Scientists interviewing at Microsoft, Thumbtack, Squarespace and others: In binary classification, how do you handle imbalanced datasets where one class significantly outnumbers the other?.

In binary classification, how do you handle imbalanced datasets where one class significantly outnumbers the other?

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