How do you deal with an unbalanced binary classification when analyzing a data set?

How to Answer
Answer Example

This is yet another operational question asking you how you react to a specific situation during a data analysis exercise. You should be able to answer this question easily as an experienced data scientist. Your answer should address the use of metrics and how they impact the analysis. Knowing this and explaining it to the Doordash interviewer will reinforce your qualifications for a data scientist's position.

"The easiest way to address an unbalanced binary classification is to review the metrics you are using in your model. Even though some metrics may be accurate, they may skew the results. Another way you can neutralize this issue is to increase the impact on the analysis for incorrectly classified and any minority class data. This results in a superior model, which produces more accurate results. Another solution is to oversample some of the minority class data or under-sample some of the majority class data, which will balance the binary classification."

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How to Answer: How do you deal with an unbalanced binary classification when analyzing a data set?

Advice and answer examples written specifically for a DoorDash job interview.

2. How do you deal with an unbalanced binary classification when analyzing a data set?

How to Answer

This is yet another operational question asking you how you react to a specific situation during a data analysis exercise. You should be able to answer this question easily as an experienced data scientist. Your answer should address the use of metrics and how they impact the analysis. Knowing this and explaining it to the Doordash interviewer will reinforce your qualifications for a data scientist's position.

Answer Example

"The easiest way to address an unbalanced binary classification is to review the metrics you are using in your model. Even though some metrics may be accurate, they may skew the results. Another way you can neutralize this issue is to increase the impact on the analysis for incorrectly classified and any minority class data. This results in a superior model, which produces more accurate results. Another solution is to oversample some of the minority class data or under-sample some of the majority class data, which will balance the binary classification."

Next Question