what does pos_label in f1_score really mean?

Question

I am trying out k_fold cross-validation in sklearn, and am confused by the pos_label parameter in the f1_score. I understand that the pos_label parameter has something to do with how to treat the data if the categories are other than binary. But I don't really have a good conceptual understanding of it's significance- does anyone have a good explanation of what it means on a conceptual level?

I have read the docs, and they didn't really help.

Answer 1

The f1 score is the harmonic mean of precision and recall. As such, you need to compute precision and recall to compute the f1-score. Both these measures are computed in reference to "true positives" (positive instances assigned a positive label), "false positives" (negative instances assigned a positive label), etc.

The pos_label parameter lets you specify which class should be considered "positive" for the sake of this computation.

More concretely, imagine you're trying to build a classifier that finds some rare events within a large background of uninteresting events. In general all you care about is how well you can identify these rare results; the background labels are not otherwise intrinsically interesting. In this case you would set pos_label to be your interesting class. If you're in a situation where you care about the results of all classes, f1_score is probably not the appropriate metric.