DOC Fix alpha param of ridge estimators
@@ -256,8 +256,9 @@ def ridge_regression(X, y, alpha, sample_weight=None, solver='auto',
Regularization strength; must be a positive float. Regularization
improves the conditioning of the problem and reduces the variance of
the estimates. Larger values specify stronger regularization.
- Alpha corresponds to ``C^-1`` in other linear models such as
- LogisticRegression or LinearSVC. If an array is passed, penalties are
+ Alpha corresponds to ``1 / (2C)`` in other linear models such as
+ :class:`~sklearn.linear_model.LogisticRegression` or
+ :class:`sklearn.svm.LinearSVC`. If an array is passed, penalties are
assumed to be specific to the targets. Hence they must correspond in
number.
@@ -618,8 +619,9 @@ class Ridge(MultiOutputMixin, RegressorMixin, _BaseRidge):
Regularization strength; must be a positive float. Regularization
improves the conditioning of the problem and reduces the variance of
the estimates. Larger values specify stronger regularization.
- Alpha corresponds to ``C^-1`` in other linear models such as
- LogisticRegression or LinearSVC. If an array is passed, penalties are
+ Alpha corresponds to ``1 / (2C)`` in other linear models such as
+ :class:`~sklearn.linear_model.LogisticRegression` or
+ :class:`sklearn.svm.LinearSVC`. If an array is passed, penalties are
assumed to be specific to the targets. Hence they must correspond in
number.
@@ -773,8 +775,9 @@ class RidgeClassifier(LinearClassifierMixin, _BaseRidge):
Regularization strength; must be a positive float. Regularization
improves the conditioning of the problem and reduces the variance of
the estimates. Larger values specify stronger regularization.
- Alpha corresponds to ``C^-1`` in other linear models such as
- LogisticRegression or LinearSVC.
+ Alpha corresponds to ``1 / (2C)`` in other linear models such as
+ :class:`~sklearn.linear_model.LogisticRegression` or
+ :class:`sklearn.svm.LinearSVC`.
fit_intercept : bool, default=True
Whether to calculate the intercept for this model. If set to false, no
@@ -1636,8 +1639,9 @@ class RidgeCV(MultiOutputMixin, RegressorMixin, _BaseRidgeCV):
Regularization strength; must be a positive float. Regularization
improves the conditioning of the problem and reduces the variance of
the estimates. Larger values specify stronger regularization.
- Alpha corresponds to ``C^-1`` in other linear models such as
- LogisticRegression or LinearSVC.
+ Alpha corresponds to ``1 / (2C)`` in other linear models such as
+ :class:`~sklearn.linear_model.LogisticRegression` or
+ :class:`sklearn.svm.LinearSVC`.
If using generalized cross-validation, alphas must be positive.
fit_intercept : bool, default=True
@@ -1752,8 +1756,9 @@ class RidgeClassifierCV(LinearClassifierMixin, _BaseRidgeCV):
Regularization strength; must be a positive float. Regularization
improves the conditioning of the problem and reduces the variance of
the estimates. Larger values specify stronger regularization.
- Alpha corresponds to ``C^-1`` in other linear models such as
- LogisticRegression or LinearSVC.
+ Alpha corresponds to ``1 / (2C)`` in other linear models such as
+ :class:`~sklearn.linear_model.LogisticRegression` or
+ :class:`sklearn.svm.LinearSVC`.
fit_intercept : bool, default=True
Whether to calculate the intercept for this model. If set
