So, again, for each category we're going to compute the precision require an f1 so

for example category c1 we have precision p1, recall r1 and F value f1.

And similarly we can do that for category 2 and and all the other categories.

Now once we compute that and we can aggregate them, so for

example we can aggregate all the precision values.

For all the categories, for computing overall precision.

And this is often very useful to summarize what we have seen in the whole data set.

And aggregation can be done many different ways.

Again as I said, in a case when you need to aggregate different values,

it's always good to think about what's the best way of doing the aggregation.

For example, we can consider arithmetic mean, which is very commonly used, or

you can use geometric mean, which would have different behavior.

Depending on the way you aggregate, you might have got different conclusions.

in terms of which method works better, so it's important to consider these

differences and choosing the right one or a more suitable one for your task.

So the difference fore example between arithmetically and

geometrically is that the arithmetically would be dominated by high

values whereas geometrically would be more affected by low values.

Base and so whether you are want to emphasis low values or

high values would be a question relate with all you And

similar we can do that for recal and F score.

So that's how we can generate the overall precision, recall and F score.