DOC: Add example links to Ridge estimators docstrings (addressing review comments)

lsakovykh · lsakovykh · commit a9cddb7422c7 · 2025-06-05T17:10:02.000+03:00
diff --git a/sklearn/linear_model/_ridge.py b/sklearn/linear_model/_ridge.py
@@ -436,6 +436,10 @@ def ridge_regression(
         reasons, using `alpha = 0` with the `Ridge` object is not advised.
         Instead, you should use the :class:`LinearRegression` object.
 
+        For an example on how the model coefficients
+        vary with the regularization strength,
+        see :ref:`<sphx_glr_auto_examples_linear_model_plot_ridge_coeffs.py>`.
+
         If an array is passed, penalties are assumed to be specific to the
         targets. Hence they must correspond in number.
 
@@ -1038,9 +1042,9 @@ class Ridge(MultiOutputMixin, RegressorMixin, _BaseRidge):
         If an array is passed, penalties are assumed to be specific to the
         targets. Hence they must correspond in number.
 
-        See :ref:`Ridge coefficients as a function of the L2 Regularization
-        <sphx_glr_auto_examples_linear_model_plot_ridge_coeffs.py>` for an example
-        on how the model coefficients vary with the regularization strength.
+        See :ref:`<sphx_glr_auto_examples_linear_model_plot_ridge_coeffs.py>`
+        for an example on how the model coefficients
+        vary with the regularization strength.
 
     fit_intercept : bool, default=True
         Whether to fit the intercept for this model. If set
@@ -1369,6 +1373,10 @@ class RidgeClassifier(_RidgeClassifierMixin, _BaseRidge):
         :class:`~sklearn.linear_model.LogisticRegression` or
         :class:`~sklearn.svm.LinearSVC`.
 
+        For an example on how the model coefficients vary
+        with the regularization strength,
+        see :ref:`<sphx_glr_auto_examples_linear_model_plot_ridge_coeffs.py>`.
+
     fit_intercept : bool, default=True
         Whether to calculate the intercept for this model. If set to false, no
         intercept will be used in calculations (e.g. data is expected to be
@@ -2537,6 +2545,10 @@ class RidgeCV(MultiOutputMixin, RegressorMixin, _BaseRidgeCV):
         :class:`~sklearn.svm.LinearSVC`.
         If using Leave-One-Out cross-validation, alphas must be strictly positive.
 
+        For an example on how the model coefficients vary
+        with the regularization strength,
+        see :ref:`<sphx_glr_auto_examples_linear_model_plot_ridge_coeffs.py>`.
+
     fit_intercept : bool, default=True
         Whether to calculate the intercept for this model. If set
         to false, no intercept will be used in calculations
@@ -2721,6 +2733,10 @@ class RidgeClassifierCV(_RidgeClassifierMixin, _BaseRidgeCV):
         :class:`~sklearn.svm.LinearSVC`.
         If using Leave-One-Out cross-validation, alphas must be strictly positive.
 
+        For an example on how the model coefficients vary
+        with the regularization strength,
+        see :ref:`<sphx_glr_auto_examples_linear_model_plot_ridge_coeffs.py>`.
+
     fit_intercept : bool, default=True
         Whether to calculate the intercept for this model. If set
         to false, no intercept will be used in calculations
