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? 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.
--
sp
On Mon, May 9, 2016 at 5:11 PM, Bharat Didwania 4-Yr B.Tech. Electrical
Engg. <bharat.didwania.ee...@itbhu.ac.in> wrote:
> 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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>
>
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