scikit-learn · jeremiedbb · Nov 24, 2022 · Aug 2, 2022 · Aug 2, 2022 · Aug 2, 2022
diff --git a/doc/modules/classes.rst b/doc/modules/classes.rst
@@ -1233,6 +1233,17 @@ Model validation
    model_selection.permutation_test_score
    model_selection.validation_curve
 
+Visualization
+-------------
+
+.. currentmodule:: sklearn
+
+.. autosummary::
+   :toctree: generated/
+   :template: class.rst
+
+   model_selection.LearningCurveDisplay
+
 .. _multiclass_ref:
 
 :mod:`sklearn.multiclass`: Multiclass classification

diff --git a/doc/modules/learning_curve.rst b/doc/modules/learning_curve.rst
@@ -94,8 +94,8 @@ The function :func:`validation_curve` can help in this case::
 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 
+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 :math:`\gamma` of an SVM on the digits dataset.
 
 .. figure:: ../auto_examples/model_selection/images/sphx_glr_plot_validation_curve_001.png
@@ -149,3 +149,21 @@ average scores on the validation sets)::
          [1. ,  0.96...,  1. ,  1. ,  0.96...],
          [1. ,  0.96...,  1. ,  1. ,  0.96...]])
 
+If you intend to plot the learning curves only, the class
+:class:`~sklearn.model_selection.LearningCurveDisplay` will be easier to use.
+You can use the method
+:meth:`~sklearn.model_selection.LearningCurveDisplay.from_estimator` similarly
+to :func:`learning_curve` to generate and plot the learning curve:
+
+.. plot::
+   :context: close-figs
+   :align: center
+
+      from sklearn.datasets import load_iris
+      from sklearn.model_selection import LearningCurveDisplay
+      from sklearn.svm import SVC
+      from sklearn.utils import shuffle
+      X, y = load_iris(return_X_y=True)
+      X, y = shuffle(X, y, random_state=0)
+      LearningCurveDisplay.from_estimator(
+         SVC(kernel="linear"), X, y, train_sizes=[50, 80, 110], cv=5)
diff --git a/doc/visualizations.rst b/doc/visualizations.rst
@@ -21,7 +21,7 @@ instead. In the following example, we plot a ROC curve for a fitted support
 vector machine:
 
 .. plot::
-   :context:
+   :context: close-figs
    :align: center
 
     from sklearn.model_selection import train_test_split
@@ -87,3 +87,4 @@ Display Objects
    metrics.DetCurveDisplay
    metrics.PrecisionRecallDisplay
    metrics.RocCurveDisplay
+   model_selection.LearningCurveDisplay
diff --git a/doc/whats_new/v1.2.rst b/doc/whats_new/v1.2.rst
@@ -510,6 +510,11 @@ Changelog
   scores will all be set to the maximum possible rank.
   :pr:`24543` by :user:`Guillaume Lemaitre <glemaitre>`.
 
+- |Feature| Added the class :class:`model_selection.LearningCurveDisplay`
+  that allows to make easy plotting of learning curves obtained by the function
+  :func:`model_selection.learning_curve`.
+  :pr:`24084` by :user:`Guillaume Lemaitre <glemaitre>`.
+
 :mod:`sklearn.multioutput`
 ..........................
 

diff --git a/examples/miscellaneous/plot_kernel_ridge_regression.py b/examples/miscellaneous/plot_kernel_ridge_regression.py
@@ -189,35 +189,27 @@
 # %%
 # Visualize the learning curves
 # -----------------------------
+from sklearn.model_selection import LearningCurveDisplay
 
-from sklearn.model_selection import learning_curve
-
-plt.figure()
+_, ax = plt.subplots()
 
 svr = SVR(kernel="rbf", C=1e1, gamma=0.1)
 kr = KernelRidge(kernel="rbf", alpha=0.1, gamma=0.1)
-train_sizes, train_scores_svr, test_scores_svr = learning_curve(
-    svr,
-    X[:100],
-    y[:100],
-    train_sizes=np.linspace(0.1, 1, 10),
-    scoring="neg_mean_squared_error",
-    cv=10,
-)
-train_sizes_abs, train_scores_kr, test_scores_kr = learning_curve(
-    kr,
-    X[:100],
-    y[:100],
-    train_sizes=np.linspace(0.1, 1, 10),
-    scoring="neg_mean_squared_error",
-    cv=10,
-)
 
-plt.plot(train_sizes, -test_scores_kr.mean(1), "o--", color="g", label="KRR")
-plt.plot(train_sizes, -test_scores_svr.mean(1), "o--", color="r", label="SVR")
-plt.xlabel("Train size")
-plt.ylabel("Mean Squared Error")
-plt.title("Learning curves")
-plt.legend(loc="best")
+common_params = {
+    "X": X[:100],
+    "y": y[:100],
+    "train_sizes": np.linspace(0.1, 1, 10),
+    "scoring": "neg_mean_squared_error",
+    "negate_score": True,
+    "score_name": "Mean Squared Error",
+    "std_display_style": None,
+    "ax": ax,
+}
+
+LearningCurveDisplay.from_estimator(svr, **common_params)
+LearningCurveDisplay.from_estimator(kr, **common_params)
+ax.set_title("Learning curves")
+ax.legend(handles=ax.get_legend_handles_labels()[0], labels=["SVR", "KRR"])
 
 plt.show()