So far we talked about classification in terms of just predicting,
is this a positive sentence or a negative sentence, is email spam or not spam,
but in general, you wanna go a little bit beyond that and
ask about, what is the probability that this is email spam?
So how confident am I on the prediction?
So if you're just looking at positive and negative,
I want to know how sure I am about the prediction.
So for example, if you take a sentence like, the sushi and
everything else were awesome.
That's a definite plus.
Definitely positive.
However, the sushi was good, the service was okay.
It's probably a plus but I'm not so sure.
It's not as definite.
And so what a classifier will often do, is not just output is positive or
negative, but output how confident, how sure it is.
One way to do that is to talk about probabilities.
So you have to play the probability of being a positive or
negative sentence, given the input sentence x.
So the output label,
what's the probability output label, given the input sentence?
So for example, for the top example there, instead of saying that's definite +,
we say the probability that it's a + given x in is 0.99.
Even x is only 0.55 because I'm kind of uncertain about that.
Predicting probabilities or level of confidence is extremely important and
as we'll see in the classification course, it allows you to do many things.
So for example when you know the probability, you can make decisions like,
what is a good decision boundary that trades off false positives and
false negatives, and balance between the two.
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