Can you talk about how precision and recall are used in the work you do?

How to Answer
Answer Example

During an interview, you will be asked about various terms, concepts, processes and procedures you use in your job. The interviewer may ask you to define them, contrast them, or give examples of how you use them. While it is good to be familiar with as many of these as possible, you may be asked about topics you don't use regularly or are unfamiliar to you. If this occurs, you should acknowledge that you are unfamiliar with the concept and then describe how you would learn about it if it is important for the job.

"Precision, also known as the positive predictive value, measures the number of positives an algorithm claims compared to the actual number of positives in the data. Recall, which is sometimes described as the true positive rate, is a similar measure but discounts data that doesn't fit the model. An example of this is if you predicted 20 girls and 5 boys in a class that actually consisted of only 20 girls, your recall rate would be 100% because there were 20 girls and the class. On the other hand, your precision would only be 80% since 5 of your predictions were wrong."

Next Question

How to Answer: Can you talk about how precision and recall are used in the work you do?

Advice and answer examples written specifically for a Machine Learning job interview.

4. Can you talk about how precision and recall are used in the work you do?

How to Answer

During an interview, you will be asked about various terms, concepts, processes and procedures you use in your job. The interviewer may ask you to define them, contrast them, or give examples of how you use them. While it is good to be familiar with as many of these as possible, you may be asked about topics you don't use regularly or are unfamiliar to you. If this occurs, you should acknowledge that you are unfamiliar with the concept and then describe how you would learn about it if it is important for the job.

Written by William Swansen on January 18th, 2021

Answer Example

"Precision, also known as the positive predictive value, measures the number of positives an algorithm claims compared to the actual number of positives in the data. Recall, which is sometimes described as the true positive rate, is a similar measure but discounts data that doesn't fit the model. An example of this is if you predicted 20 girls and 5 boys in a class that actually consisted of only 20 girls, your recall rate would be 100% because there were 20 girls and the class. On the other hand, your precision would only be 80% since 5 of your predictions were wrong."

Written by William Swansen on January 18th, 2021

Next Question