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...@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 > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > -- Guillaume Lemaitre Scikit-learn @ Inria Foundation https://glemaitre.github.io/
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