Error diffusion

Monochrome error diffusion

We'll let R do the work.

We're almost at the correlation matrix.

That's just dividing by N.

Consisting of accidental and systematic errors.

It's pretty cool.

Because those correlations were based on samples that had a broader range of cognitive ability.

And now I have a variance co variance matrix.

Remember sigma.

Variance

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

The mean is now a vector with 1 element for each of the 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.

We're just standardizing, the variances to be one.

We removed the outliers using a very context dependent method of just thresholding.

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.

And, if X and Y overlap, if they co vary.

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.

The mean is easily calculated it's 2 and 4.

You can see it over here 10, 8, 6, 4, and 2.

And the co variances to fall between negative one and positive one essentially to be correlations, alright.

We take the product of the variance of x and the variance of y modally with the normalizers and we get something quadratic even in variance space.

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.

But this one is just like the variance calculation but it mixes x's and y's whereas these are x² and y².

The Pearson product moment correlation coefficient, little r.

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.