MNT Revert "DOC precise stopping criteria for coordinate descent"
This reverts commit
@@ -750,7 +750,7 @@ class ElasticNet(MultiOutputMixin, RegressorMixin, LinearModel):
The tolerance for the optimization: if the updates are
smaller than ``tol``, the optimization code checks the
dual gap for optimality and continues until it is smaller
- than ``tol``, see Notes below.
+ than ``tol``.
warm_start : bool, default=False
When set to ``True``, reuse the solution of the previous call to fit as
@@ -816,12 +816,6 @@ class ElasticNet(MultiOutputMixin, RegressorMixin, LinearModel):
To avoid unnecessary memory duplication the X argument of the fit method
should be directly passed as a Fortran-contiguous numpy array.
- The precise stopping criteria based on `tol` are the following: First, check that
- that maximum coordinate update, i.e. :math:`\\max_j |w_j^{new} - w_j^{old}|`
- is smaller than `tol` times the maximum absolute coefficient, :math:`\\max_j |w_j|`.
- If so, then additionally check whether the dual gap is smaller than `tol` times
- :math:`||y||_2^2 / n_{\text{samples}}`.
-
Examples
--------
>>> from sklearn.linear_model import ElasticNet
@@ -1187,7 +1181,7 @@ class Lasso(ElasticNet):
The tolerance for the optimization: if the updates are
smaller than ``tol``, the optimization code checks the
dual gap for optimality and continues until it is smaller
- than ``tol``, see Notes below.
+ than ``tol``.
warm_start : bool, default=False
When set to True, reuse the solution of the previous call to fit as
@@ -1264,12 +1258,6 @@ class Lasso(ElasticNet):
assumed to be specific to the targets. Hence they must correspond in
number.
- The precise stopping criteria based on `tol` are the following: First, check that
- that maximum coordinate update, i.e. :math:`\\max_j |w_j^{new} - w_j^{old}|`
- is smaller than `tol` times the maximum absolute coefficient, :math:`\\max_j |w_j|`.
- If so, then additionally check whether the dual gap is smaller than `tol` times
- :math:`||y||_2^2 / n_{\text{samples}}`.
-
Examples
--------
>>> from sklearn import linear_model
