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DOC add links to examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.py (scikit-learn#28895)
Co-authored-by: bivav <bivavraj@gmail.com>
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doc/modules/linear_model.rst

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@@ -530,6 +530,7 @@ The class :class:`ElasticNetCV` can be used to set the parameters
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* :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_and_elasticnet.py`
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* :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_coordinate_descent_path.py`
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* :ref:`sphx_glr_auto_examples_linear_model_plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.py`
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|details-start|
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**References**

sklearn/linear_model/_coordinate_descent.py

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@@ -776,6 +776,9 @@ class ElasticNet(MultiOutputMixin, RegressorMixin, LinearModel):
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Whether to use a precomputed Gram matrix to speed up
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calculations. The Gram matrix can also be passed as argument.
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For sparse input this option is always ``False`` to preserve sparsity.
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Check :ref:`an example on how to use a precomputed Gram Matrix in ElasticNet
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<sphx_glr_auto_examples_linear_model_plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.py>`
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for details.
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max_iter : int, default=1000
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The maximum number of iterations.

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