Does it make sense to calculate the recall for each sample in a multilabel classification problem?
Suppose I have 3 data samples, each having its own true set of labels and predicted set of labels.
I want to see the match between the true set of labels and the predicted set of labels. I do not care for the true negatives or false positives in each prediction, so this translates to recall score for me. Programmatically, I would do an AND operation between y_predicted and y_true to get the number of true positives and divide it by the total number of true labels for each sample. (in other words, true positives/(true positives+false negatives))
My question is -
Is calculating recall per sample (not per label), usually done?
Is my thought process correct?
I've seen articles where a single recall is calculated for the whole matrix of y_true and y_predicted or recall is calculated for a single label.