Hi Farhad!



 farhadzadeh:

how I can measure the performance?


There is no single measure that tells you everything you want to know
about multilabel-classification performance.  See this brief overview in
Wikipedia’s “Multi-label classification” article:
Statistics and evaluation metrics 2
Different use cases will care more or less about different kinds of errors,
and, so, will prefer different performance measures.


Particularly for no finding labels?


If you care about not missing “no finding” cases, you could look at an
accuracy given by the percentage of true “no finding” cases that were
predicted (completely) correctly.
If you really care about not incorrectly predicting “not no finding” cases
as being “no finding,” you could look at a rather weird sort of “accuracy”
given by the percentage of “not no finding” cases that were predicted to
have some labels even if all of the predicted labels were wrong.  (Of
course if this were your use case you would likely be better off training
a simple “no finding” vs. “not no finding” binary classifier.)
Good luck.
K. Frank