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

You still have the highest arrest rate in your precinct?

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

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

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

And, yes, the answer is 0.108.

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

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

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

Now let me ask you a really tricky question.

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And that is how we compare different algorithms.

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

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

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

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

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

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.

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