[MRG+1] DOC more detailed note on SVC and SVR scalability #13209
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qinhanmin2014
approved these changes
Feb 21, 2019
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LGTM |
chkoar
reviewed
Feb 21, 2019
sklearn/svm/classes.py
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| @@ -431,7 +431,9 @@ class SVC(BaseSVC): | |||
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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
Outdated
| @@ -431,7 +431,9 @@ class SVC(BaseSVC): | |||
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|
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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 | |||
| 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
reviewed
Feb 21, 2019
sklearn/svm/classes.py
Outdated
| 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
approved these changes
Feb 27, 2019
sklearn/svm/classes.py
Outdated
| @@ -431,7 +431,10 @@ class SVC(BaseSVC): | |||
|
|
|||
| 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 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
requested changes
Feb 28, 2019
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@qinhanmin2014 is right there I think. It seems to be a typo.
sklearn/svm/classes.py
Outdated
| @@ -431,7 +431,10 @@ class SVC(BaseSVC): | |||
|
|
|||
| 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 datasets with more than a couple of 10000 samples. For large | |||
| datasets consider using :class:`sklearn.linear_model.LinearSVR` or | |||
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Suggested change
| datasets consider using :class:`sklearn.linear_model.LinearSVR` or | |
| datasets consider using :class:`sklearn.linear_model.LinearSVC` or |
sklearn/svm/classes.py
Outdated
| 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
approved these changes
Mar 6, 2019
qinhanmin2014
approved these changes
Mar 11, 2019
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Thanks for addressing the review comment @qinhanmin2014 ! (and for other reviews) |
xhluca
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Apr 28, 2019
koenvandevelde
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Jul 12, 2019
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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.