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, May 9, 2016 at 4:05 AM, Shishir Pandey <shishir...@gmail.com> wrote:
> 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_score>>> y_pred = [0, 2, 1, 3]
>
> I am assuming that here each dimension is a label and the entry
> represents how many times that label appears. Also I am assuming that
>
> the each entry has weight of 1.
>
>
>
>
> >>> y_true = [0, 1, 2, 3]
>
>
> Then, A \intersection B (y_pred and y_true) will be = 1 + 1 + 3 = 5
>
> and A \union B will be = 3 + 3 + 3 = 9
>
> How is the jaccard similarity = 0.5?
>
>
> >>> jaccard_similarity_score(y_true, y_pred)0.5>>>
> >>> jaccard_similarity_score(y_true, y_pred, normalize=False)2
>
>
> --
> sp
>
>
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