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When there's a trade off between precision and recall, it's important for
us to look at the two extremes.
What does it mean to have a classifier that's extremely precise?
And what does it mean to have a classifier that's extremely high recall?
And how the two can go against each other sometimes.
First, let's think about what I call an optimistic classifier.
You might know some of these optimists in your life.
They think everything is good.
How's it going?
Good. Even if bad stuff is happening,
they say good.
Those folks say that all possible experiences are good, so
they're optimists.
That means that pretty much every input, every sentence,
is labeled as positive, very few get labeled as negative.
It's extremely likely that all the truly positive data points get labeled as good.
What does that mean?
That means that I have perfect recall,
because I recall all those positive data points.
Good.
But I might not get
perfect precision because I put in a bunch of negatives into that bit.
How can we address that?
We can have that pessimistic classifier,
you might have some friends like that where you try really hard,
you do everything for them, you go out of your way, and everything sucks.
Every single experience that you have is really bad.
There's very, very, very, very, few things that they say are good.
And when they are there very likely to be good but everything else they say is bad,
so everything else in the world is very hard, equals -1.
Pessimist means that you're going to miss out on many good things in life.
The pessimists have high precision because the few things that was good tends to be
good, but very, very, very low recall, they don't inspire great things in life.
It turns out there is a spectrum between a high precision low recall model and
low precision high recall model, the pessimist and the optimist.
What we'd like to do is somehow balance between the two perspectives in the world
to find something that's just right for us.
So, balance between a pessimistic model and the optimistic model.
In particular, we want to find as many positive reviews or sentences as possible,
as many of those as possible, with as few incorrect predictions as we can.
So, that's the balance we're trying to strike in the case of our restaurant.
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