Precision-Recall Tradeoff in Real-World Use Cases

Ace your ML interview by quickly understanding which real-world use cases demand higher precision, and which ones demand a higher recall and why?

Why you should read this article?

All machine learning interviews expect you to understand the practical application of precision-recall tradeoff in real-world use cases, beyond just the definitions and formulas.

I have tried to capture this essence by defining a 🔑 “secret key” that you can exploit to ace your next ML interview and impress your interviewer by providing articulate justifications!

Definitions

💡 Precision measures that out of all the positive predicted examples, how many detections were correct?

💡 Recall measures that out of all actual positive examples, how many were we able to identify?

Make sure you completely understand the above definitions because all our further discussions will build upon this. Remembering just these two definitions is sufficient to answer any ML interview question related to precision and recall that comes your way. Yes, you can, trust me!

You also do not even need to memorize the (very) confusing formulas for precision, recall, true positive rate, false positive rate, specificity, sensitivity, and the list goes on…
So, let’s go ahead and convert the above definitions into mathematical formulas.