A city fit for a king

Cats fit on the windowsill

Finally, given this hypothesis representation, what we need to do, as before, is fit the parameters theta to our data.

So this seems reasonable so far.

Children fit in the snow

I don't fit in anywhere I go ?

Here is what we see coming.

Now let's consider the model selection problem.

It did not let up.

like vacuum tube technologies, and relay based technologies.

Either way they were going to eat it.

Some crazy guy is giving a commencement speech.

It seems like a reasonable fit to the data, because, you know, it

We keep the inner slope, but change the outer slope.

And the hose on that van is just about a perfect match...

So, if you take this training set and fill a straight

So the we have the neutron.

So Jawbone is a project that you're familiar with, and it has a humanistic technology.

1 WATCH Here is a friar, and slaughter'd Romeo's man,

And fit in all the right places with lots and lots of star sapphires.

lambda is large, then you have a high bias problem and you might not fit your training set so well.

Concretely, if you fit some set of parameters theta 0, theta 1, theta 2 and so on to your training set then, the fact that your hypothesis does well in the training set, well, this doesn't mean much in terms of predicting how well your hypothesis will generalize to new examples not seen in the training set.

But somehow adding that example out there caused linear regression to change in straight line fit to the data from this magenta line out here to this blue line over here, and caused it to give us a worse hypothesis.

So, for example, we could test the full model, We could test the fit of that relative to a model that takes out the direct effect.

An ill beseeming semblance for a feast.

Here we see Hubble diagrams with the main trend line subtracted a particular model subtracted and then residuals fit for variety fodder models.

He trusts to you to set them free, Exactly as we were.

In later videos, we will build on this to talk about how to prevent in the problems of overfitting and underfitting as well.

But settling isn't always such a bad thing.

So, you can fit things like, you know, access a

X1 through X M, we then fit the parameters, mu 1 through mu n, and sigma squared 1 through sigma squared n, and so these were the formulas similar to the formulas we have in the previous video, that we're going to use the estimate each of these parameters, and just to give some interpretation, mu J, that's my average value of the j feature.

Heh heh heh.

Now, so this is how we fit the parameters to this data.

He was moaning a bit.

It's perfectly alright, but...

Yeah, that's it.

About side dish in my bentou...

Nonetheless, if you obtain a surface brightness profile of the many, many ellipticals and then if you fit Sersic law to all of them and plot the value of the

lambda corresponds a high bias where you might not even fit your training set well, whereas a small value of lambda corresponds to, if you can you know freely fit to very high degree polynomials, your data, let's say.

We've already seen a lot of times the problem of overfitting, in which just because the

Here, drink

Heh heh heh. Best in Texas.

Across then Down

Down then Across

Mm, uhhuh.