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" wrote:
>
On 9 May 2016 9:47 pm, "Shishir Pandey" 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.
>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.
I want to understand what do your examples represent? Could you give an
example where the dimension
On 9 May 2016 5:24 pm, "Shishir Pandey" wrote:
>
> This is what I am having trouble understanding. What does each dimension
of the vector represent? I am thinking of it as follows:
>
> [label_1, label_2, ..., label_N]
>
> a characteristic vector would be something like [1, 1, 0, ..., 1, 0, 0]
>
>
On 5/9/2016 7:53 AM, Shishir Pandey wrote:
> A 0 [in both of] 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.
Yes I agree; that would constitute a standard definition.
Alan Isaac
-
This is what I am having trouble understanding. What does each dimension of
the vector represent? I am thinking of it as follows:
[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?
Hi,
jaccard similarity coefficient or score is the ratio of size of
intersection to the size of union of the to label sets .
In this case the size of union is 4 and that of intersection is 2 . Hence
the jaccard similarity score will be 2/4=0.5.
I hope this will help.
Regards,
Bharat.
On Mon, M
Hi,
If I understand it correctly, the jaccard similarity is the ratio of number
of matching outputs to the total number of outputs in case of binary and
multiclass classification. Here, the first and the last outputs are
matching among the four outputs, hence the jaccard score is 2/4=0.5.
I hope
I a bit confused regarding the Jaccard similarity score. The example given
on :
http://scikit-learn.org/stable/modules/generated/sklearn.metrics.jaccard_similarity_score.html#sklearn.metrics.jaccard_similarity_score
>>> import numpy as np>>> from sklearn.metrics import
>>> jaccard_similarity_sco
