Thanks for your reply. I get it now.
The all zeros case implies that the two sets are empty. Which is a 0/0
situation. Hence, it is taken to be 1.
--
sp
On Mon, May 9, 2016 at 10:11 PM, Maniteja Nandana <
maniteja.modesty...@gmail.com> wrote:
>
> On 9 May 2016 9:47 pm, "Shishir Pandey" <shishir...@gmail.com> wrote:
> >
> > From what you are saying isn't the Jaccard distance for the multi-class
> case equivalent to the (1-hammingloss). Where the hamming loss is the
> average of places where the two vectors are different.
> >
> Yeah, from what I can understand you are right. Accuracy score, zero one
> loss and hamming loss are all equivalent in this case.
>
> > I want to understand what do your examples represent? Could you give an
> example where the dimension is y is 2 x 3 because I getting confused on
> what the 2 represents, is it the number of columns or number of rows?
> >
>
> In multi label classification, prediction will be a 2D array of 0 and 1s.
> It's shape is (n_outputs, n_labels). So, a 2X3 array represents 2 outputs
> and 3 labels possible for each of them. A 1 represents that the label is
> present for that output and 0 is otherwise.
>
> Hence for jaccard, it sees the number of common labels across the
> labels(column) in y_true and y_pred and divides it with the number of
> labels present in at least one of y_true and y_pred. The weighted average
> is then calculated across all outputs(rows).
>
> So for the first example above, the first output has [0, 1] and [1, 1] as
> the labels. Hence it is 1/2 =0.5 while second output has both as [1, 1]. So
> it is 1. While averaged, it becomes 0.75.
>
> PS: I am not aware of the exact reason, but in case both y_true and y_pred
> are all zeros ([0, 0]) for an output, the jaccard score is taken as 1 in
> the implementation.
>
> Hope it helps.
>
> Regards,
> Maniteja.
>
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