Jeff Kao

I think if you take any basic machine learning courses, you'll learn about decision trees.

So the concepts are really the same.

And essentially it's a way to do a couple of different functions versus classification.

So if I receive an email, does it seem like a positive sentiment or a negative sentiment or a neutral sentiment?

It does interpolation.

So given the name and the country and I don't know, the sports that somebody does, try and guess their height.

You know, it's like, you know,

four feet to, I don't know, seven feet, something like that.

And that's interpolation.

It does learn to rank, which is particularly interesting.

And as I mentioned, we do, you know, from the inverted index, we do ranking, which in some sense is like really just a primitive of classification.

But essentially what it's saying is like, given this list of entities, which one has the highest rank and which one has the lowest rank?

And you can really just think of that as like pairwise comparison.

So like if I'm given, if I type in 841 Broadway,

And I'm in this latitude and longitude.

Is it referring to 841 Broadway in Brooklyn or 841 Broadway in Manhattan?

And so that is essentially what learn to rank is.

But you can apply it to more entities than just two.

But really, it is just sort of classification if you think about it.

Yes, there is.