Machine Learning | Multi Label Evaluation Metrics

We consider 0/1 loss, Log loss and accuracy as three metrics to check the goodness of multi-label classification.
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[@RanjiRaj18]

For notes👉 github.com/ranjiGT/ML-latex-amendments

[@NoName-iz8td]

thank you for your explanation, but for the accuracy if we have for example ground true is a=[0,0,0] and prediction is b=[0,0,0] then the union of a and b is [0,0,0] which is impossible for the denominator !! how can you handel such case. In addition to, we should consider the false lables also. if the model predicts false and it is false then the model has already done well but in this situation doesn't consider

[@yassinmechbal323]

Can you give us the Implementation of Hamming Loss in python language using Data science Libraries

[@bhavinpatel7261]

Can you Share MEKA multilabel classification vedio..plz..