Some crazy guy is giving a commencement speech.

Cats fit on the windowsill

Children fit in the snow

I don't fit in anywhere I go ?

A city fit for a king

Here is what we see coming.

Now let's consider the model selection problem.

So this seems reasonable so far.

It did not let up.

Either way they were going to eat it.

like vacuum tube technologies, and relay based technologies.

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

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

He was moaning a bit.

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

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

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.

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

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

'Nothing can be clearer than THAT.

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.

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.

So, the mission statement and the vision of Coursera fits perfectly with my own mission and vision of teaching Statistics and this is why I do it for living.

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

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

Invariably, Mr. Rainsford, invariably they choose to hunt.

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

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.

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

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

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

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

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

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

And this global minimum corresponds to this hypothesis, which gives me a good fit to the data.

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