case equivalent to the (1-hammingloss). Where the hamming loss is the

average of places where the two vectors are different.

*Post by Shishir Pandey*This is what I am having trouble understanding. What does each dimension

[label_1, label_2, ..., label_N]

a characteristic vector would be something like [1, 1, 0, ..., 1, 0, 0]

This represents weather label_i is present in the set or not? In that

case the answer would be different. A 0 is the two sets would represent

that the label is not present in either of the sets and hence the union

would be smaller than the dimension of the vector.

Sorry if I am misunderstanding here but in case you are referring to multi

label classification here, something like an array of [[0,0],[0,1]] would

be the value of predicted outputs, but an array of [0,1,3,2] would

represent multi class output and is used in the example in discussion.

Regarding the size of union, in multiclass and binary, intersection size

is the number of times predicted class is same by total number of outputs.

Here 0 need not mean the absence of class.

But in multi label 0 means absence of label and in this case jaccard

similarity is calculated for each output and weighted mean is calculated.

In following, the first output has only one label in ground truth while

two in prediction. While in the second example has the first output which

has only one label in both ground truth and prediction.

y_true = [[0, 1], [1, 1]]

y_pred = [[1, 1], [1, 1]]

jaccard_similarity_score

0.75 #(0.5 + 1)/2

y_true = [[0, 1], [1, 1]]

y_pred = [[0, 1], [1, 1]]

jaccard_similarity_score

1#(1+1)/2

Hope it helps.

Regards,

Maniteja.

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