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

This is where the concept of ordinary lease squar es regression comes in. And the idea is very simple.

When we fit the parameters of our learning algorithm we think about choosing the parameters to minimize the training error.

Consisting of accidental and systematic errors.

It's already zero for the original setting.

Variance

Error diffusion

One over here, and one over here.

Not surprisingly the best complexity is obtained where the generalization error is minimum.

Look for smaller changes

Then you can run our command run( ) with our parameters, and whatever it outputs is our best error so far.

Then you've got a really big range.

Monochrome error diffusion

Having done that, finally, what

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

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.

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

J C V and J

It goes from two forty down to two hundred.

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

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

If your hypothesis basically classified the example y correctly.

However, in practice you're often just given the training data error.

It doesn't change it quite enough, but that protein acts just a little bit different.

Expressed through standard deviation Values given over accidental errors.

Values given over accidental errors.

Yeah. I would agree with that.

It's a ratio of sum relationship that you observed relative to what you would expect due to chance.

The answer is this.

learning algorithm and it is not performing as well as your are hoping, so your cross validation set error or your test set error is high.

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

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.

Really, that's a sum of square distances, right? Of all of these errors.

It has to do with the fact that in a quadratic version of the error,

But then the tricky part is the distances.

The baby held out his tiny hand.

As N gets really, really large.

The idea is to use gradient descent.

Let's multiply both sides of this equation by dx.

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

The unstandardized regression coefficient, B.

It was 22 miles.

So of course, the difference between the actual score Y, or observed score Y and predicted score Y is error.