[MUSIC] Now that explored the idea of precision
let's talk about the slightly more subtle idea of recall, and recall has to do
with out of the possible positive things in the world, everything out there,
did I find every possible positive? Sentence about my restaurant. So, more specifically, I might take
all the restaurant reviews and all the sentences and
feed it through my classifier. And then classify some of those as
positive, and some of those as negative. So, some might have, y hat = +1. Some might have y hat = -1. Now, if I look at the true label, the true
class of each one of the sentences, so yi, which ones have +1, you see that four
of those were predicted to be positive, and just like we talked
earlier in the module. But there turned out to be two other
sentences, there were three positive that my algorithm did not find
that fell into the negative bin. So those are missed out. So in other words,
we found the 4 positive sentences, but we missed out on 2 positive sentences
that could have shown in my website. And maybe those two sentences were so amazing they would change the history
of my restaurant, but I missed it. Because they did not have perfect recall. So, more formally out of all
the truly positive data points, so the one's where yi is +1, we can have
a subset on those that we do capture. So where y hat is also +1 but
there's a subset which we don't capture where we think the y hat
is -1, so y hat does not agree with y. And so, that's the part that we missed. So the recall is the fraction of
the once that we actually get. We want everything to be
in the blue box here. More formally, we can define recall
as the fraction of true positives. For these are the data points
that we were positive and we got them right divided
by the true positives. And the false negative. So the data points that were true,
but we labeled as negative. So falsely labeled as negative. And so, this is going to have value
one if the false negatives are zero. Which means we captured everything,
we captured all the true positives. And zero if we did not
capture any true positives so all the positive data went
to the false negative bin. So if we go back to the example
that we have been looking at, I want to show positive
sentences on my website. I've got four of them, in y hat i equals
+1 but I missed out on two sentences. So for example, I missed out on the sentences that said my
wife tried the ramen and it was delicious, and so maybe somebody's interested in
ramen they don't see that sentence, they don't go to my website, and so
I missed out on something really good. So high recall means that you discover
everything positive that's being said about the restaurant or
all of the positive data points. [MUSIC]