I have a multi-class multi-label classification problem where there are 4 classes (happy, laughing, jumping, smiling) and each class can be positive:1 or negative:0. An input can belong to more than one class . So let’s say that for an input x , the actual labels are [1,0,0,1] and the predicted labels are [1,1,0,0]. So how to calculate the precision, recall and f1 score for this fine grained approach? Are there any predefined methods to do this?