Consisting of accidental and systematic errors.

Variance

Error diffusion

Yeah. I would agree with that.

Monochrome error diffusion

Having done that, finally, what

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.

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

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

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.

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

Now let's write out deviations for each term.

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.

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.

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

The test has to be wrong.

It's your probability of rejecting the null when in fact you should.

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.

What we'll also see is the standard error association, associated with each regression coefficient, and that will give us a T test.

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.

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

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.

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

Of course, we won't do that. We'll just do it in R.

Here's what I mean.

I've discovered the test has a margin of error.

There's your margin of error.

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

Suppose we run a particle filter with N equals 1 particle. What will happen?

Sometimes there is an alternative test sets metric that might be easier to interpret, and that's the misclassification error.

Expressed through standard deviation Values given over accidental errors.

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

Positive reading must have been a hiccup.