I don't fit in anywhere I go ?

A city fit for a king

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.

Cats fit on the windowsill

Either way they were going to eat it.

So this seems reasonable so far.

Children fit in the snow

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

Here is what we see coming.

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

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

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

Now let's consider the model selection problem.

It did not let up.

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.

like vacuum tube technologies, and relay based technologies.

An ill beseeming semblance for a feast.

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.

Some crazy guy is giving a commencement speech.

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

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

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

He was moaning a bit.

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

So the we have the neutron.

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.

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

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.

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

Suppose you like to decide what degree of polynomial to fit to a data set, sort of what features to include to give you a learning algorithm.

And in fact, if you compare it to data, it fits very well with the development of cities and economies.

Let's look at some more examples.

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

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

Here's one more, you know, at this value of theta zero, and at that value of theta one, we end up with this hypothesis, h(x) and again, not a great fit to the data, and is actually further away from the minimum.

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

The supervised learning problem in this case is given a set of labels to fit a hypothesis to it.

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.

Is a value that's out here that's you know pretty far from the minimum right it's pretty far this is a pretty high cost because this is just not that good a fit to the data.

We could trade off the relative way between how much we want to fit the training set well, as minimizing A, versus how much we care about keeping the values of the parameters small.

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