Error diffusion

Monochrome error diffusion

Consisting of accidental and systematic errors.

Remember sigma.

Variance

We're going to use these throughout this entire course are standard deviation and variance.

Having done that, finally, what

And if you have a high variance problem, your training set error will usually be low, that is much lower than the cross validation error.

As we know, this gives us an estimated mean.

I've got in my example minus the mean, minus the center, minus the mean of distribution.

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

This is the standard deviation.

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 there's a lot of variance in the population, then most likely we'll have more of sampling error than if there's a small degree of variance in the population.

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.

That's where the idea of standard error comes in.

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

There's variance in the sample SD squared.

J C V and J

The answer is this.

For each dot in a scatter plot, if we look at the difference, the distance between the dot and the regression line, that represents the prediction error or the residual.

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.

These were the results for Jeremy Lin.

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.

We'll let R do the work.

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.

So the standard deviation of the Ys is 6.

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.

This finishes my class on the 3 Ms.

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.