Looking at check_array in the SVR and SVC, we convert to CSR format if the sparse matrices are not from this format:

https://github.com/scikit-learn/scikit-learn/blob/b194674c4/sklearn/svm/_base.py#L146

Basically, this is more efficient because we are going to make operation which will get row.,

In scikit-learn most predictor expect CSR apart of tree-based where CSC will be more efficient. CSC is also the format

which is better for the preprocessing estimator (in general). Be aware that we are going to convert to the appropriate

format if required.

On Wed, 29 Jan 2020 at 02:54, Peng Yu <pengyu.ut@gmail.com> wrote:

https://scikit-learn.org/stable/modules/svm.html

Of the svm classes mentioned above, which sparse matrixes are
appropriate to be used with them?

https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html#scipy.sparse.csr_matrix

It is not very clear what matrix operations are used in fit(), so I
can not tell what sparse matrixes should be used. Thanks.

--
Regards,
Peng
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Guillaume Lemaitre

Scikit-learn @ Inria Foundation
https://glemaitre.github.io/