Cats fit on the windowsill

Children fit in the snow

Here is what we see coming.

Now let's consider the model selection problem.

It did not let up.

I don't fit in anywhere I go ?

Either way they were going to eat it.

A city fit for a king

like vacuum tube technologies, and relay based technologies.

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

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

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

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.

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

So the we have the neutron.

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

So this seems reasonable so far.

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

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

I just can't go on any more.

gt gt Chris Yerga I think he wants to work on his abs, so he's gonna pick the flat abs.

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

But settling isn't always such a bad thing.

The image will be scaled to fit the desktop. It will be stretched to fit all four corners.

Some crazy guy is giving a commencement speech.

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.

So there, the model would have been all three equations.

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

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

In all

This hypothesis, h(x), with these values of theta zero, theta one, it's really not such a good fit to the data.

But I heard him exclaim, ere he drove out of sight,

You are encouraged to experiment with the different styles until you find one which best suits your pattern of work.

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.

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

Undo All

For everything.

And the Vaucouleurs profile looks like a straight line in those coordinates.

But I'm not curious just about B, I want to think about ALL the points.

On All Desktops

Everything's in here.

Let's look at some more examples.

I want every street... every house... every backyard.

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.