There is only the training data.

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

So we've now computed the era term's delta 4 for our network.

label yi from this specific example we're looking at to compute the error term for delta L for the output there.

Have you allowed for any error in your calculation?

Error Creating Diff

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

And the way we estimate that is to rely on the principles, of the central limit theorem, and calculate standard error, by looking at the standard deviation, in our sample, and the size, of our sample.

So one fundamental lesson to take away from that simple example is look at your residuals.

Consisting of accidental and systematic errors.

Calculated estimate variance

The way to get to that confidence interval, and we'll see this as we, as we do the analyses in R is you just take your point estimate and you add some value based on sampling error and standard error.

Variance

Error diffusion

And so what you do is take your parameter theta that you have learned from the training set, and plug it in here and compute your test set error.

So again, the values of the coefficients are estimated such that the model comes up with optimal predictions.

Monochrome error diffusion

Having done that, finally, what

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

Multiply that times 50, you get 25,000, which is within one percent of the actual diameter of the Earth.

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.

Because if you do, you're wrong,

A miscalculation on my part.

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.

Here is now finally the revelation.

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

If you calculate with the right number of ads 4 instead of 64 you get a much more realistic figure.

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So standard error, represents the overall average amount of sampling error, in a particular study.