8000 [MRG] update _alpha_grid class in _coordinate_descent.py by JJmistry · Pull Request #15835 · scikit-learn/scikit-learn · GitHub
[go: up one dir, main page]
Skip to content

[MRG] update _alpha_grid class in _coordinate_descent.py #15835

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Dec 8, 2019
Merged

[MRG] update _alpha_grid class in _coordinate_descent.py #15835

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
14 changes: 7 additions & 7 deletions sklearn/linear_model/_coordinate_descent.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -43,34 +43,34 @@ def _alpha_grid(X, y, Xy=None, l1_ratio=1.0, fit_intercept=True,
y : ndarray, shape (n_samples,)
Target values

Xy : array-like, optional
Xy : array-like, default=None
Xy = np.dot(X.T, y) that can be precomputed.

l1_ratio : float
l1_ratio : float, default=1.0
The elastic net mixing parameter, with ``0 < l1_ratio <= 1``.
For ``l1_ratio = 0`` the penalty is an L2 penalty. (currently not
supported) ``For l1_ratio = 1`` it is an L1 penalty. For
``0 < l1_ratio <1``, the penalty is a combination of L1 and L2.

eps : float, optional
eps : float, default=1e-3
Length of the path. ``eps=1e-3`` means that
``alpha_min / alpha_max = 1e-3``

n_alphas : int, optional
n_alphas : int, default=100
Number of alphas along the regularization path

fit_intercept : boolean, default True
fit_intercept : boolean, default=True
Whether to fit an intercept or not

normalize : boolean, optional, default False
normalize : boolean, default=False
This parameter is ignored when ``fit_intercept`` is set to False.
If True, the regressors X will be normalized before regression by
subtracting the mean and dividing by the l2-norm.
If you wish to standardize, please use
:class:`sklearn.preprocessing.StandardScaler` before calling ``fit``
on an estimator with ``normalize=False``.

copy_X : boolean, optional, default True
copy_X : boolean, optional, default=True
If ``True``, X will be copied; else, it may be overwritten.
"""
if l1_ratio == 0:
Expand Down
0