A city fit for a king

I don't fit in anywhere I go ?

Cats fit on the windowsill

Now let's consider the model selection problem.

So this seems reasonable so far.

Children fit in the snow

Here is what we see coming.

like vacuum tube technologies, and relay based technologies.

Either way they were going to eat it.

Some crazy guy is giving a commencement speech.

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

It did not let up.

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

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

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

So the we have the neutron.

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

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

An ill beseeming semblance for a feast.

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.

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

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

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.

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

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

But settling isn't always such a bad thing.

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

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

He was moaning a bit.

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.

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.

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

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

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

We must try to get rid of it, the sister now said decisively to the father, for the mother, in her coughing fit, was not listening to anything.

I just can't go on any more.

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

line of the ellipses like these, but you certainly cannot fit a more complex data set like that shown here.

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

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

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

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

This was the first time that majority of physicists really started paying attention and taking this possibility seriously.

'Nothing can be clearer than THAT.