DOC fix back references to removed example (#30059)

glemaitre · web-flow · commit 30f7d8a5fc6a · 2024-10-13T21:09:21.000Z
diff --git a/doc/modules/learning_curve.rst b/doc/modules/learning_curve.rst
@@ -42,7 +42,7 @@ this reason, it is often helpful to use the tools described below.
 .. rubric:: Examples
 
 * :ref:`sphx_glr_auto_examples_model_selection_plot_underfitting_overfitting.py`
-* :ref:`sphx_glr_auto_examples_model_selection_plot_validation_curve.py`
+* :ref:`sphx_glr_auto_examples_model_selection_plot_train_error_vs_test_error.py`
 * :ref:`sphx_glr_auto_examples_model_selection_plot_learning_curve.py`
 
 
@@ -115,14 +115,7 @@ to :func:`validation_curve` to generate and plot the validation curve:
 If the training score and the validation score are both low, the estimator will
 be underfitting. If the training score is high and the validation score is low,
 the estimator is overfitting and otherwise it is working very well. A low
-training score and a high validation score is usually not possible. Underfitting,
-overfitting, and a working model are shown in the in the plot below where we vary
-the parameter `gamma` of an SVM with an RBF kernel on the digits dataset.
-
-.. figure:: ../auto_examples/model_selection/images/sphx_glr_plot_validation_curve_001.png
-   :target: ../auto_examples/model_selection/plot_validation_curve.html
-   :align: center
-   :scale: 50%
+training score and a high validation score is usually not possible.
 
 .. _learning_curve:
 
diff --git a/sklearn/model_selection/_validation.py b/sklearn/model_selection/_validation.py
@@ -2414,7 +2414,7 @@ def validation_curve(
 
     Notes
     -----
-    See :ref:`sphx_glr_auto_examples_model_selection_plot_validation_curve.py`
+    See :ref:`sphx_glr_auto_examples_model_selection_plot_train_error_vs_test_error.py`
 
     Examples
     --------
