Pushing the docs to dev/ for branch: main, commit 17c84a87d4f92495e2b1be2f77a6e862aed6c68d

Author: sklearn-ci

dev/_downloads/02192f99342d6d1323161978b6c80bfc/plot_ols_ridge.zip

@@ -1,7 +1,7 @@
 """
-========================================
-Label Propagation digits active learning
-========================================
+=========================================
+Label Propagation digits: Active learning
+=========================================
 
 Demonstrates an active learning technique to learn handwritten digits
 using label propagation.

dev/_downloads/02fe21a1f5d14bd1ebbe34aa905d9bf6/plot_multilabel.zip

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Label Propagation learning a complex structure\n\nExample of LabelPropagation learning a complex internal structure\nto demonstrate \"manifold learning\". The outer circle should be\nlabeled \"red\" and the inner circle \"blue\". Because both label groups\nlie inside their own distinct shape, we can see that the labels\npropagate correctly around the circle.\n"
+        "\n# Label Propagation circles: Learning a complex structure\n\nExample of LabelPropagation learning a complex internal structure\nto demonstrate \"manifold learning\". The outer circle should be\nlabeled \"red\" and the inner circle \"blue\". Because both label groups\nlie inside their own distinct shape, we can see that the labels\npropagate correctly around the circle.\n"
       ]
     },
     {

dev/_downloads/0358b1921962fd7c6f5a94c5abc91476/plot_hashing_vs_dict_vectorizer.zip

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Label Propagation digits active learning\n\nDemonstrates an active learning technique to learn handwritten digits\nusing label propagation.\n\nWe start by training a label propagation model with only 10 labeled points,\nthen we select the top five most uncertain points to label. Next, we train\nwith 15 labeled points (original 10 + 5 new ones). We repeat this process\nfour times to have a model trained with 30 labeled examples. Note you can\nincrease this to label more than 30 by changing `max_iterations`. Labeling\nmore than 30 can be useful to get a sense for the speed of convergence of\nthis active learning technique.\n\nA plot will appear showing the top 5 most uncertain digits for each iteration\nof training. These may or may not contain mistakes, but we will train the next\nmodel with their true labels.\n"
+        "\n# Label Propagation digits: Active learning\n\nDemonstrates an active learning technique to learn handwritten digits\nusing label propagation.\n\nWe start by training a label propagation model with only 10 labeled points,\nthen we select the top five most uncertain points to label. Next, we train\nwith 15 labeled points (original 10 + 5 new ones). We repeat this process\nfour times to have a model trained with 30 labeled examples. Note you can\nincrease this to label more than 30 by changing `max_iterations`. Labeling\nmore than 30 can be useful to get a sense for the speed of convergence of\nthis active learning technique.\n\nA plot will appear showing the top 5 most uncertain digits for each iteration\nof training. These may or may not contain mistakes, but we will train the next\nmodel with their true labels.\n"
       ]
     },
     {

dev/_downloads/038d1885eb5f5ea53aca42da3031fe38/plot_document_clustering.zip

@@ -1,7 +1,7 @@
 """
-==============================================
-Label Propagation learning a complex structure
-==============================================
+=======================================================
+Label Propagation circles: Learning a complex structure
+=======================================================
 
 Example of LabelPropagation learning a complex internal structure
 to demonstrate "manifold learning". The outer circle should be