Pushing the docs to dev/ for branch: main, commit 5599dcd4701dacd5c57674aeb7d82b2475ceb5db

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -90,3 +90,15 @@
 # The estimator can now run on any Array API compliant libraries such as
 # `CuPy <https://docs.cupy.dev/en/stable/overview.html>`__, a GPU-accelerated array
 # library. For details, see the :ref:`User Guide <array_api>`.
+
+# %%
+# Improved efficiency of many estimators
+# --------------------------------------
+# In version 1.1 the efficiency of many estimators relying on the computation of
+# pairwise distances was greatly improved for float64 dense input. In version 1.2,
+# the efficiency of these estimators was further improved for all combinations of
+# float32/float64 and dense/sparse input (for all metrics except euclidean). It
+# concerns essentially clustering, manifold learning and neighbor search algorithms.
+# A detailed list of the impacted estimators can be found in the
+# :ref:`changelog <changes_1_2>`. The main benefits are a reduced memory footprint
+# and a much better scalability on multi-core machines.

dev/_downloads/138e7c706c17949c3098ff8074b03ce7/plot_release_highlights_1_2_0.py

@@ -78,6 +78,13 @@
       "source": [
         "## Experimental Array API support in :class:`~discriminant_analysis.LinearDiscriminantAnalysis`\nExperimental support for the [Array API](https://data-apis.org/array-api/latest/)\nspecification was added to :class:`~discriminant_analysis.LinearDiscriminantAnalysis`.\nThe estimator can now run on any Array API compliant libraries such as\n[CuPy](https://docs.cupy.dev/en/stable/overview.html)_, a GPU-accelerated array\nlibrary. For details, see the `User Guide <array_api>`.\n\n"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Improved efficiency of many estimators\nIn version 1.1 the efficiency of many estimators relying on the computation of\npairwise distances was greatly improved for float64 dense input. In version 1.2,\nthe efficiency of these estimators was further improved for all combinations of\nfloat32/float64 and dense/sparse input (for all metrics except euclidean). It\nconcerns essentially clustering, manifold learning and neighbor search algorithms.\nA detailed list of the impacted estimators can be found in the\n`changelog <changes_1_2>`. The main benefits are a reduced memory footprint\nand a much better scalability on multi-core machines.\n\n"
+      ]
     }
   ],
   "metadata": {