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DOC Add link to plot_lasso_and_elasticnet.py example in linear model … #30585

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DOC Add link to plot_lasso_and_elasticnet.py example in linear model … #30585

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11 changes: 9 additions & 2 deletions sklearn/linear_model/_coordinate_descent.py
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Expand Up @@ -848,7 +848,7 @@ class ElasticNet(MultiOutputMixin, RegressorMixin, LinearModel):
SGDRegressor : Implements elastic net regression with incremental training.
SGDClassifier : Implements logistic regression with elastic net penalty
(``SGDClassifier(loss="log_loss", penalty="elasticnet")``).

Notes
-----
To avoid unnecessary memory duplication the X argument of the fit method
Expand All @@ -859,7 +859,10 @@ class ElasticNet(MultiOutputMixin, RegressorMixin, LinearModel):
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}}`.


For an example showcasing Elastic-Net alongside Lasso and ARDRegression for sparse
signal recovery in the presence of noise and feature correlation, see:
:ref:`sphx_glr_auto_examples_linear_model_plot_lasso_and_elasticnet.py`.
Examples
--------
>>> from sklearn.linear_model import ElasticNet
Expand Down Expand Up @@ -1264,6 +1267,10 @@ class Lasso(ElasticNet):
LassoCV : Lasso alpha parameter by cross-validation.
LassoLarsCV : Lasso least angle parameter algorithm by cross-validation.
sklearn.decomposition.sparse_encode : Sparse coding array estimator.

For an example comparing Lasso with other L1-based regression models
(Elastic-Net and ARDRegression) on sparse and noisy data, see:
:ref:`sphx_glr_auto_examples_linear_model_plot_lasso_and_elasticnet.py`.

Notes
-----
Expand Down
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