Pushing the docs to dev/ for branch: main, commit 439bda4752f6208f30938b534d3cb93fbdc4f285

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -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()