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
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