When is precision more important over recall?

Data Science, AI and ML
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#1
  • Precision : This tells when you predict something positive, how many times they were actually positive. whereas,
  • Recall : This tells out of actual positive data, how many times you predicted correctly.

Having said above, in case of spam email detection, One should be okay if a spam email (positive case) left undetected and doesn’t go to spam folder but , if an email is good (negative), then it must not go to spam folder. i.e. Precison is more important. (If model predicts something positive (i.e. spam), it better be spam. else, you may miss important emails).

  • For rare cancer data modeling, anything that doesn’t account for false-negatives is a crime. Recall is a better measure than precision.
#2

Precision is more important than recall when you would like to have less False Positives in trade off to have more False Negatives. Meaning, getting a False Positive is very costly, and a False Negative is not as much.

In a zombie apocalypse, of course you would try to accept as many as healthy people you can into your safe zone, but you really don’t want to mistakenly pass a zombie into the safe zone. So if your method causes some of the healthy people mistakenly not to get into the safe zone, then so be it.