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

I don't fit in anywhere I go ?

Here is what we see coming.

Cats fit on the windowsill

Children fit in the snow

A city fit for a king

So this seems reasonable so far.

like vacuum tube technologies, and relay based technologies.

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.

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.

An ill beseeming semblance for a feast.

Now let's consider the model selection problem.

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

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

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

It did not let up.

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

But settling isn't always such a bad thing.

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.

Either way they were going to eat it.

Some crazy guy is giving a commencement speech.

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.

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

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

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.

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

I just can't go on any more.

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

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.

So the we have the neutron.

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

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

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

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

Let's look at some more examples.

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

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

We usually cannot see fainter than some given threshold, and so if you look deeper and deeper, at some point you are going to start losing objects that are just below the detection threshold.

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

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

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

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

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.