4

2

The precision is defined as

$$\text{precision} = \frac{\text{true positive}}{\text{true positive} + \text{false positive}}$$

Is there any definition what this value should be if there is no positive classification (but of course positive elements)?

So the classifier would predict all negative elements when tested? Whatever the classifier model class, your estimate from a test set would show "this classifier is much the same as guessing negative in all cases". I doubt there is any default other than the arithmetic one where $\frac{0}{0}$ is not defined. – Neil Slater – 2016-07-09T19:27:00.957

@NeilSlater Yes, I am talking about a "classifier" which "predicts" always just the most common class (the negative one, in this case). – Martin Thoma – 2016-07-09T19:33:38.883