So given a dataset

Electronegativity

The tuberculin reaction was negative.

Give me two units, Onegative.

The results of the test were negative.

look at a data set

If you are clean, I will give you every last dime.

Is equal to one and theta transpose x(i) is less than or equal to minus one whenever you have a negative example and it turns out that when you solve this optimization problem, when you minimize this as a function of the parameters theta you get a very interesting decision boundary.

Now, let's do one last modification and let's write this procedure assuming you observed a negative test result.

The assignment of the 2 classes, you know, spam, no spam, and so on the assignment of the 2 classes to positive and negative, to 0 and 1 is somewhat arbitrary and it doesn't really matter.

A 70 sensitivity and less than that for specificity

And what's the same for P( C, Neg).

The probability of a positive test and having cancer.

Now comes the hard part

I think we better replace the negative power coupling.

And as you go up, those are the bigger trials, so they've got less error in them.

And a couple of videos ago, we said oxygen is a lot more electronegative than the hydrogen.

Think of the cancer non cancer as the robot position and think of the positive as whether the colored door observed is the correct one.

SARS, metapneumovirus, HlV, RSV all these.

Shade

So, it ends up at a different location on the figure.

Our first gives us the following factors 0.9 and 0.2.

Cloud and shade

Everything is a conspiracy theory!

So, I had a 100 percent sensitivity.

Of one woman's constant need to show the world... her labia.

So, let me write down cancer and test and let me go through different possibilities.

Those are all below 10 ng for mL.

Now, the same algorithm works if your test says negative.

The probability of receiving a positive test result for either test is 0.9.

You are a snake!

It's pretty bleak.

I guess in plural it's scrotal, like medium and media.

Great.

And this one over here takes a moment to computer.

And, if you include enough polynomial terms then, you know, maybe you can get a hypotheses that separates the positive and negative examples.

He was the brains behind the plot.

If you remember this lecture tomorrow and I hope you will it's because of brain plasticity.

Now, what's my probability having cancer or not? But now all the positives in here become negatives.

Because, you know, these are our positive examples, these are our negative examples.

I believe it says the power coupling on the negative axis has been polarized.

Flat shading

And this is just the implicit differentiation that you

We're fine, thank God.

How are you getting on, Aksinya?