Stage magic relies almost wholly on the momentum error.

That was just a kind of a what if conversation.

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

And so that's the definition of the test set error using the misclassification error of the 0 1 misclassification metric.

I find that the best results in life are often held back by false constructs and untested assumptions.

Again, what do you think could help us get the right answer this time?

Consisting of accidental and systematic errors.

So he wasn't drunk...

And then what's the derivative of y squared with respect to x?

This is our original y.

So what I did in yellow, both this bright yellow and this

Setup Virtual Machine

Hypothetically.

Because it is a little bit of a game.

It is open .. it is open to subjectivity, and a lot of that subjectivity is all around the assumptions that you choose to make.

This is what I assume about annual maintenance.

Well what's this term going to do?

Kathleen's idea,

So what I do is I figure out, well, given your assumptions

Take, for instance, what you carry, 9 mm hollowpoint.

So this is all just running descriptives to figure out if you truly have normal distributions.

I have no need of that hypothesis.

Don't torture yourself with hypotheticals.

So we can see the ratio of this to this.

So let's apply this cocky attitude to the problem of multiplying two integers.

I have a normal coin.

So if we evaluate this, this will give us the standard deviation of this number.

But for the sake of simplicity ... ... we're going to assume ... ... that it was kind of a constant velocity ... ... or what we are calculating is going to be his average velocity.

Check that box if you think that would be helpful.

Warning Assuming normal speed.

Assuming, of course, they text each other.

Wrong top level tag

And comparing heights of the tallest building in two cities, that's a deterministic number.

When you're done with this, you arrive at a number that now combines the cancer hypothesis with the test result.

So before I go on with how we're going to approach this problem,

So, what do we do now?

So, now that we have defined the training validation or cross validation and test sets, we can also define the training error, cross validation error, and test error.

We get a particular h(x). And this is, this is not quite at the minimum, but it's pretty close.

You can configure how many virtual desktops there are.

But maybe y is equal to e to some constant r, times x.

John's hypothetical man

Okay, what assumptions did you make?

Seems the logical assumption.

Here's what I mean.

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.