It really gives me a 0.8 chance of getting a negative result if I don't have cancer.

But an association The higher people's blood levels of vitamin D are, the less heart disease they have, the less cancer.

And then we asked them again,

Mammography isn't perfect, but it's the only test that's been proven to reduce mortality from breast cancer.

I'd like to have a test for breast cancer.

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

We're able to detect some fraction of the two motorbikes so here, for example, at this point, the precision is 0.7.

And, yes, the answer is 0.108.

I'd like to have a Pap smear done.

You still have the highest arrest rate in your precinct?

Their average precision is 58 . And something like potted plant is really hard to detect and the average precision there is sixteen .

Just to check suppose probability of cancer is 0.1, the sensitivity is 0.9, specificity is 0.8.

One of these regions is called the left inferior frontal gyrus.

And that is how we compare different algorithms.

But if someone started off saying,

R. For now, power is simply the probability that you'll reject the null hypothesis when in fact you should.

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So, precision and recall. These terms have the following meaning.

And, let me use a... So precision here is on the Y axis.

And here is how we do this for the case of, say a motorcycle detector.

And using the prior, we know that P of not C is 0.99.

The bottom row's more difficult, and we'll estimate those probabilities based on power.

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So if you get a positive test result you're going to raise the probability of having cancer relative to the prior probability.

Now let me ask you a really tricky question.

Precisions means what fraction of the detections that you declared are actually true motorcycles.

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And then we told them the average likelihood of someone like them to suffer these misfortunes.

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Here you get 0.72, which is the product of not having cancer in the first place 0.9 and the probability of getting a negative test result under the condition of not having cancer.

In this video we talked about how to estimate p of x, the probability of x, for the purpose of developing an anomaly detection algorithm.

And then, we also measure recall. Recall is, how many of the bicycles in the data set did we manage to find?

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