Hi,
I realized that the fit_transform method of TfidfVectorizer returns a CSR
matrix, which supports array indexing, while CountVectorizer returns a COO
matrix, which doesn't. I always liked the clean and interchangeable nature
of sklearn, so I wondered, whether it would break other pieces if we would
return a CSR matrix in CountVectorizer as well. Or is performance a concern
here?
CountVectorizer's fit_transform:
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/feature_extraction/text.py#L530
TfidfVectorizer's fit_transform:
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/feature_extraction/text.py#L942
Thanks,
wr
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