• Home
  • Recent Q&A
  • Java
  • Cloud
  • JavaScript
  • Python
  • SQL
  • PHP
  • HTML
  • C++
  • Data Science
  • DBMS
  • Devops
  • Hadoop
  • Machine Learning
in Data Science by
Q:

Explain what a false positive and a false negative are. Why is it important these from each other? Provide examples when false positives are more important than false negatives, false negatives are more important than false positives and when these two types of errors are equally important

1 Answer

0 votes
by

A false positive is an incorrect identification of the presence of a condition when it’s absent.

A false negative is an incorrect identification of the absence of a condition when it’s actually present.

An example of when false negatives are more important than false positives is when screening for cancer. It’s much worse to say that someone doesn’t have cancer when they do, instead of saying that someone does and later realizing that they don’t.

This is a subjective argument, but false positives can be worse than false negatives from a psychological point of view. For example, a false positive for winning the lottery could be a worse outcome than a false negative because people normally don’t expect to win the lottery anyways.

Related questions

+1 vote
asked May 28 in Data Science by sharadyadav1986
+1 vote
asked May 28 in Data Science by sharadyadav1986
...