Pushing the docs to dev/ for branch: main, commit 0f8a7775ad248b9aa4be63291ae71d9212a46e6c

Author: sklearn-ci

dev/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 10004e4ad06b5587b4adde4c3a2e0879
+config: a65a6fe570c78743acc0f5705db216d7
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -191,6 +191,7 @@ def highlight_min(x):
 # outliers and overfits less.
 #
 # .. _calibration-section:
+#
 # Calibration of the confidence interval
 # --------------------------------------
 #

dev/_downloads/2f3ef774a6d7e52e1e6b7ccbb75d25f0/plot_gradient_boosting_quantile.py

@@ -65,7 +65,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Visualize Comparision\nIt includes three subplots:\n1. Plotting training errors of both models over boosting iterations.\n2. Plotting validation errors of both models over boosting iterations.\n3. Creating a bar chart to compare the training times and the estimator used\nof the models with and without early stopping.\n\n"
+        "## Visualize Comparison\nIt includes three subplots:\n\n1. Plotting training errors of both models over boosting iterations.\n2. Plotting validation errors of both models over boosting iterations.\n3. Creating a bar chart to compare the training times and the estimator used\n   of the models with and without early stopping.\n\n\n"
       ]
     },
     {
@@ -90,7 +90,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Summary\nIn our example with the :class:`~sklearn.ensemble.GradientBoostingRegressor`\nmodel on the California Housing Prices dataset, we have demonstrated the\npractical benefits of early stopping:\n\n- **Preventing Overfitting:** We showed how the validation error stabilizes\nor starts to increase after a certain point, indicating that the model\ngeneralizes better to unseen data. This is achieved by stopping the training\nprocess before overfitting occurs.\n\n- **Improving Training Efficiency:** We compared training times between\nmodels with and without early stopping. The model with early stopping\nachieved comparable accuracy while requiring significantly fewer\nestimators, resulting in faster training.\n\n"
+        "## Summary\nIn our example with the :class:`~sklearn.ensemble.GradientBoostingRegressor`\nmodel on the California Housing Prices dataset, we have demonstrated the\npractical benefits of early stopping:\n\n- **Preventing Overfitting:** We showed how the validation error stabilizes\n  or starts to increase after a certain point, indicating that the model\n  generalizes better to unseen data. This is achieved by stopping the training\n  process before overfitting occurs.\n- **Improving Training Efficiency:** We compared training times between\n  models with and without early stopping. The model with early stopping\n  achieved comparable accuracy while requiring significantly fewer\n  estimators, resulting in faster training.\n\n"
       ]
     }
   ],

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -173,7 +173,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Errors are higher meaning the models slightly overfitted the data. It still\nshows that the best test metric is obtained when the model is trained by\nminimizing this same metric.\n\nNote that the conditional median estimator is competitive with the squared\nerror estimator in terms of MSE on the test set: this can be explained by\nthe fact the squared error estimator is very sensitive to large outliers\nwhich can cause significant overfitting. This can be seen on the right hand\nside of the previous plot. The conditional median estimator is biased\n(underestimation for this asymmetric noise) but is also naturally robust to\noutliers and overfits less.\n\nCalibration of the confidence interval\n--------------------------------------\n\nWe can also evaluate the ability of the two extreme quantile estimators at\nproducing a well-calibrated conditional 90%-confidence interval.\n\nTo do this we can compute the fraction of observations that fall between the\npredictions:\n\n"
+        "Errors are higher meaning the models slightly overfitted the data. It still\nshows that the best test metric is obtained when the model is trained by\nminimizing this same metric.\n\nNote that the conditional median estimator is competitive with the squared\nerror estimator in terms of MSE on the test set: this can be explained by\nthe fact the squared error estimator is very sensitive to large outliers\nwhich can cause significant overfitting. This can be seen on the right hand\nside of the previous plot. The conditional median estimator is biased\n(underestimation for this asymmetric noise) but is also naturally robust to\noutliers and overfits less.\n\n\n## Calibration of the confidence interval\n\nWe can also evaluate the ability of the two extreme quantile estimators at\nproducing a well-calibrated conditional 90%-confidence interval.\n\nTo do this we can compute the fraction of observations that fall between the\npredictions:\n\n"
       ]
     },
     {

dev/_downloads/8452fc8dfe9850cfdaa1b758e5a2748b/plot_gradient_boosting_early_stopping.ipynb

@@ -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.