False positive demystified

It happens sometimes when your colleagues come from a non-English language talking in a common language such as English, there are phrases which don’t readily convey what you want to say.

The other day, when a friend of mine used “It’s a false positive” so suggest something, the other colleague gave a puzzled expression “What’s a false positive?”. Up until that point, I must admit neither did I have a clear understanding, but looking at the two words – false, positive – I could muster up an intuitive explanation on the spot.

But back in my mind, I wasn’t really sure if I did explain the meaning simply using the dictionary definitions of those two words apart. I mean, it isn’t hard. But it was one of those phrases that you want to know more about with some level of confidence & clarity. Maybe it’s just me.

The other day, when working with some study materials for a course about machine learning, I came across a brilliant piece of explanation that finally answered the meaning of “false positive” in one fell swoop. Take a look at this table below

Does it make sense? Absolutely yes – but I’ll explain if it doesn’t.

The prediction is a machine learning result saying something is correct or incorrect (in my example the problem the machine learning is trying to solve/predict is if a patient has cancer or not looking at the size of the tumor & patient’s age). The actual is what you know – based on evidence or tests done beforehand proving that the patient did indeed have cancer or not. Lining up correct/incorrect with what the prediction says against what actually is makes the understanding about “false positive” much more clearer to me now.

So, if you thought something was correct but it actually isn’t then it’s a false positive.

The other way round, if you thought something was incorrect but it turned out to be correct then it’s a false negative.

Makes much more sense now.