DOC more detailed note on SVC and SVR scalability
@@ -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.LinearSVC` or
+ :class:`sklearn.linear_model.SGDClassifier` instead, possibly after a
+ :class:`sklearn.kernel_approximation.Nystroem` transformer.
The multiclass support is handled according to a one-vs-one scheme.
@@ -791,7 +794,12 @@ class SVR(BaseLibSVM, RegressorMixin):
The free parameters in the model are C and epsilon.
- 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 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
+ :class:`sklearn.kernel_approximation.Nystroem` transformer.
Read more in the :ref:`User Guide <svm_regression>`.
