DOC fixes sphinx warning due to rendering issue (scikit-learn#27951)

glemaitre · web-flow · commit 0f8a7775ad24 · 2023-12-12T17:01:18.000Z
diff --git a/examples/ensemble/plot_gradient_boosting_early_stopping.py b/examples/ensemble/plot_gradient_boosting_early_stopping.py
@@ -112,13 +112,15 @@
     val_errors_with.append(mean_squared_error(y_val, val_pred))
 
 # %%
-# Visualize Comparision
-# ---------------------
+# Visualize Comparison
+# --------------------
 # It includes three subplots:
+#
 # 1. Plotting training errors of both models over boosting iterations.
 # 2. Plotting validation errors of both models over boosting iterations.
 # 3. Creating a bar chart to compare the training times and the estimator used
-# of the models with and without early stopping.
+#    of the models with and without early stopping.
+#
 
 fig, axes = plt.subplots(ncols=3, figsize=(12, 4))
 
@@ -170,11 +172,10 @@
 # practical benefits of early stopping:
 #
 # - **Preventing Overfitting:** We showed how the validation error stabilizes
-# or starts to increase after a certain point, indicating that the model
-# generalizes better to unseen data. This is achieved by stopping the training
-# process before overfitting occurs.
-#
+#   or starts to increase after a certain point, indicating that the model
+#   generalizes better to unseen data. This is achieved by stopping the training
+#   process before overfitting occurs.
 # - **Improving Training Efficiency:** We compared training times between
-# models with and without early stopping. The model with early stopping
-# achieved comparable accuracy while requiring significantly fewer
-# estimators, resulting in faster training.
+#   models with and without early stopping. The model with early stopping
+#   achieved comparable accuracy while requiring significantly fewer
+#   estimators, resulting in faster training.
diff --git a/examples/ensemble/plot_gradient_boosting_quantile.py b/examples/ensemble/plot_gradient_boosting_quantile.py
@@ -191,6 +191,7 @@ def highlight_min(x):
 # outliers and overfits less.
 #
 # .. _calibration-section:
+#
 # Calibration of the confidence interval
 # --------------------------------------
 #

Original file line number	Diff line number	Diff line change
`@@ -191,6 +191,7 @@ def highlight_min(x):`
`191`	`191`	`# outliers and overfits less.`
`192`	`192`	`#`
`193`	`193`	`# .. _calibration-section:`
	`194`	`+#`
`194`	`195`	`# Calibration of the confidence interval`
`195`	`196`	`# --------------------------------------`
`196`	`197`	`#`