If I were to just flip this over like this, so we're

So that's the plane.

So if my plane looks like this I know it's very hard to read now and you wanted the intersection of this plane, this green plane and the cone I should probably redraw it all, but hopefully you're not getting overwhelmingly confused the intersection would look like this.

Consisting of accidental and systematic errors.

It's the quadratic deviation from the mean.

Remember, every sample statistic is just a point estimate.

Test are just the average squared error, or one half of the average squared error on my training validation and test sets without the extra regularization chart.

Variance

Error diffusion

The variance by its very nature computes in the quadratic.

So this is my three dimensional plane.

You can almost view that I'm pivoting around this point, at the intersection of this point and the plane and the cone.

Monochrome error diffusion

Having done that, finally, what

There's variance in the sample SD squared.

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

We have very good data since 1962 1960 about on the size of families in all countries.

So the mean for this sample is 5.47, the mean for the population was 5.5.

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.

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.

Values given over accidental errors.

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

I'm drawing it in such a way that it only intersects this bottom cone and the surface of the plane is parallel to the side of this top cone.

Plane

Plane

plane

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.

isosurface

Iso Surface

Flat Map

If you draw a curve like this, your quadratic error becomes 2.

If I go to my output for 100 steps, I find that the y value is between 0.7 and 0.9.

The unstandardized regression coefficient, B.

It was 22 miles.

In particular, it could invent things like a curve which fills the plane.

Yeah. I would agree with that.

Let's look at the training error and cause validation error and plot them on this figure.

There's always a little fluctuation from sample to sample.

It is equal to the square root of the sum of the deviations in the y's over the deviations of the x's.

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