Whereas, your cross validation error, assuming that this say the squared error which we're trying to minimize.

So make no mistake about it.

Consisting of accidental and systematic errors.

Variance

Error diffusion

Right, that's sampling error. And how much sampling error we expect on average is standard error.

I'm definitely not going back home!

Monochrome error diffusion

Having done that, finally, what

Although, you know, it was wonderful.

Concretely, for the high bias case, that is, the case of under fitting, what we find is that both the cross validation error and the training error are going to be high.

Give or take a few years

There's variance in the sample SD squared.

Concretely, let our training error and cross validation error be defined as in the previous videos, just say, the squared error, the average squared error as measured on the 20 sets or as measured on the cross validation set.

If your hypothesis basically classified the example y correctly.

If the prediction of our function f equals our target label, and the error is zero, then no update occurs.

Leave you again

I finally met

Yeah, superspecial sure.

And one thing we could do is then take these parameters and look at the test set error.

So, if your algorithm is suffering from a bias problem, the training set error would be high and you may find that the cross validation error will also be high.

Never Ever Ever!

Values given over accidental errors.

And then j subscript cv is my cause validation error is pretty much what you'd expect.

Do, 'cause we should definitely...

Absolutely!

You haven't thought about that?

Definitely!

Definitely.

Never!

Never!

Obviously. No.

Ever?

J C V and J

The answer is this.

And similarly, I'm then also going to define the cross validation set error when the test set error, as before to be the average sum of squared errors on the cross validation and the test sets.

Looks like I got you by the balls, Abby.

absolute

You have carte blanche.

I'm sure of it.

Then, as to money? You have carte blanche.

You're smart because you have something to offer.

I should have had more faith in his pure, fine spirit.

Does this luxurious flat belong to my nephew Francis?

All we have to do is to go to the shop, hunt up the date when it was bought, and the thing's done.