[MRG+1] DOC more detailed note on SVC and SVR scalability#13209
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qinhanmin2014 merged 4 commits intoscikit-learn:masterfrom Mar 11, 2019
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[MRG+1] DOC more detailed note on SVC and SVR scalability#13209qinhanmin2014 merged 4 commits intoscikit-learn:masterfrom
qinhanmin2014 merged 4 commits intoscikit-learn:masterfrom
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qinhanmin2014
approved these changes
Feb 21, 2019
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LGTM |
chkoar
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Feb 21, 2019
sklearn/svm/classes.py
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| The implementation is based on libsvm. The fit time complexity | ||
| is more than quadratic with the number of samples which makes it hard | ||
| to scale to dataset with more than a couple of 10000 samples. | ||
| to scale to dataset with more than a couple of 10000 samples. For large |
sklearn/svm/classes.py
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| The implementation is based on libsvm. | ||
| The implementation is based on libsvm. The fit time complexity | ||
| is more than quadratic with the number of samples which makes it hard | ||
| to scale to dataset with more than a couple of 10000 samples. For large |
ogrisel
reviewed
Feb 21, 2019
sklearn/svm/classes.py
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| is more than quadratic with the number of samples which makes it hard | ||
| to scale to dataset with more than a couple of 10000 samples. | ||
| to scale to dataset with more than a couple of 10000 samples. For large | ||
| datasets consider using :class:`LinearSVC` or :class:`SGDClassifier` |
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we need the full module path for the link to work, no? sklearn.linear_model.SGDClassifier
ogrisel
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Feb 21, 2019
sklearn/svm/classes.py
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| to scale to dataset with more than a couple of 10000 samples. | ||
| to scale to dataset with more than a couple of 10000 samples. For large | ||
| datasets consider using :class:`LinearSVC` or :class:`SGDClassifier` | ||
| instead. |
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possibly after a class:sklearn.kernel_approximation.Nystroem transformer.
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Addressed both comments. |
jnothman
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Feb 27, 2019
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I like this. I wonder if it's explicit enough in the user guide
sklearn/svm/classes.py
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| is more than quadratic with the number of samples which makes it hard | ||
| to scale to dataset with more than a couple of 10000 samples. | ||
| to scale to datasets with more than a couple of 10000 samples. For large | ||
| datasets consider using :class:`sklearn.linear_model.LinearSVR` or |
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use classifiers instead of regressors?
glemaitre
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Feb 28, 2019
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@qinhanmin2014 is right there I think. It seems to be a typo.
sklearn/svm/classes.py
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| is more than quadratic with the number of samples which makes it hard | ||
| to scale to dataset with more than a couple of 10000 samples. | ||
| to scale to datasets with more than a couple of 10000 samples. For large | ||
| datasets consider using :class:`sklearn.linear_model.LinearSVR` or |
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| datasets consider using :class:`sklearn.linear_model.LinearSVR` or | |
| datasets consider using :class:`sklearn.linear_model.LinearSVC` or |
sklearn/svm/classes.py
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| to scale to dataset with more than a couple of 10000 samples. | ||
| to scale to datasets with more than a couple of 10000 samples. For large | ||
| datasets consider using :class:`sklearn.linear_model.LinearSVR` or | ||
| :class:`sklearn.linear_model.SGDRegressor` instead, possibly after a |
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Suggested change
| :class:`sklearn.linear_model.SGDRegressor` instead, possibly after a | |
| :class:`sklearn.linear_model.SGDClassifier` instead, possibly after a |
agramfort
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Mar 6, 2019
qinhanmin2014
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Mar 11, 2019
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Thanks for addressing the review comment @qinhanmin2014 ! (and for other reviews) |
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This extends the note in the SVC / SVR on their bad scalability with the suggestion to use LinearSVC/LinearSVR or SDG classifier/regressor on larger datasets.
Just saw some usage of
SVC(kernel='linear')on large datasets, so putting it in the docstring in addition to the user manual might be useful.