The description of LassoLarsCV compared the number of samples with the number of observations, but it was meant to compare the number of samples to the number of features (or dimensions) of the data. I changed "observations" to "features" in the following sentence:
> However, :class:`LassoLarsCV` has the advantage of exploring more relevant values of `alpha` parameter, and
if the number of samples is very small compared to the number of observations, it is often faster than :class:`LassoCV`.
* DOC adding a warning on the relation between C and alpha
* DOC removing extra character
* DOC: changes to the relation described
* DOC fixing typo
* DOC fixing typo
* DOC fixing link to Ridge
* DOC link enhancement
* DOC fixing line length
* Link to SAG paper updated
link to SAG paper in `References` section updated (#7512).
* Link to SAG paper updated
dead link of SAG paper in Logistic Regression user guide updated.
resolves#7512
* SAG paper cited in docstring
SAG paper cited in `Reference` section of docstring(#7512).
Add gradient calculation in _huber_loss_and_gradient
Add tests to check the correctness of the loss and gradient
Fix for old scipy
Add parameter sigma for robust linear regression
Add gradient formula to robust _huber_loss_and_gradient
Add fit_intercept option and fix tests
Add docs to HuberRegressor and the helper functions
Add example demonstrating ridge_regression vs huber_regression
Add sample_weight implementation
Add scaling invariant huber test
Remove exp and add bounds to fmin_l_bfgs_b
Add sparse data support
Add more tests and refactoring of code
Add narrative docs
review huber regressor
Minor additions to docs and tests
Minor fixes that deals with dealing with NaN values in targets
and old verions of SciPy and NumPy
Add HuberRegressor to robust estimator
Refactored computation of gradient and make docs render properly
Temp
Remove float64 dtype conversion
trivial optimizations and add a note about R
Remove sample_weights special_casing
address @amueller comments
Setting precompute to "auto" was found to be slower when n_samples > n_features
since the computation of the Gram matrix is computationally expensive and
outweighs the benefit of fitting the Gram for just one alpha.
Michele Lacchia