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

I don't fit in anywhere I go ?

A city fit for a king

like vacuum tube technologies, and relay based technologies.

Cats fit on the windowsill

It did not let up.

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

Either way they were going to eat it.

Some crazy guy is giving a commencement speech.

So this seems reasonable so far.

Children fit in the snow

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 is what we see coming.

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.

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

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

Now let's consider the model selection problem.

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

So the we have the neutron.

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

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

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

An ill beseeming semblance for a feast.

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.

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

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

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.

Let's look at some more examples.

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.

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

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

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

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

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

He was moaning a bit.

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

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.

If we have a very high degree of polynomial, our training error is going to be really low.

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.

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

In the previous video, we talked about the form of the hypothesis for linear regression with multiple features or with multiple variables.

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.

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