[MUSIC] Well, the last thing that I, I want to
cover in this module is the fact that we've looked at a very simple notion of
errors, this residual sum of squares. And it's actually really,
really commonly used in practice. But we're gonna talk a lot more about different types of errors
later in the course. But I wanted to go through some of
the intuition of what happens if we use a different measure of air. Okay so this residual sum of
squares that we've been looking at Is something that's called
a symmetric cost function. And that's because what we're assuming
when we look at this aerometric is the fact that if we over
estimate the value of our house, that has the same cost as if we under
estimate the value of the house. And the question is so
we actually believe that is the case? And what happens if there might not
be symmetric cost to these error? But what if the cost of
listing my house sales price as too high is bigger than the cost
if I listed it as too low? So for example,
if I list the value as too high, then maybe no one will
even come see the house. Or they come see it and
they say oh it's definitely not worth it. I'm not putting in an offer. So the result might be I get no offers. So as a seller, that's a big cost to me. I've gone through this whole
thing of trying to sell my house. I want or need to sell my house. And I get no offers. On the other hand, if I list the sales price as too low,
of course I won't get offers as high as I could have if I had more accurately
estimated the value of the house. But I still get offers, and maybe that cost to me Is less bad
than getting no offers at all. For example if I have to move to
another state I have no choice but to sell my house. Okay, so in this case it might be more appropriate
to use an asymmetric cost function where the errors are not weighed equally
between these two types of mistakes. And instead of this dash orange line here, which represents our fit when we're
minimizing residual sum of squares. I will get some different solution. Again, sorry,
I love to write over my animations. Here this is the fit minimizing
residual sum of squares, and this other orange line here is this
other solution using an asymmetric loss. Asymmetric Loss and
specifically in asymmetric loss. Let me just say asymmetric cost. Where I prefer to underestimate
the value than over. And that's what you see here is,
in general, we're predicting the values as lower. As compared to the line that we got or our
predictions using residual sum of squares. Okay so that's just a little bit of
intuition about what would happen using different cost functions and again we're gonna talk a lot more
about this later on in this course. [MUSIC]