Pushing the docs to dev/ for branch: main, commit 411416149fa2fa5e95f6fe7a874e5455fea7f582

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -4,18 +4,18 @@
 ============================================================
 
 Evaluate the ability of k-means initializations strategies to make
-the algorithm convergence robust as measured by the relative standard
+the algorithm convergence robust, as measured by the relative standard
 deviation of the inertia of the clustering (i.e. the sum of squared
 distances to the nearest cluster center).
 
 The first plot shows the best inertia reached for each combination
-of the model (``KMeans`` or ``MiniBatchKMeans``) and the init method
+of the model (``KMeans`` or ``MiniBatchKMeans``), and the init method
 (``init="random"`` or ``init="kmeans++"``) for increasing values of the
 ``n_init`` parameter that controls the number of initializations.
 
-The second plot demonstrate one single run of the ``MiniBatchKMeans``
+The second plot demonstrates one single run of the ``MiniBatchKMeans``
 estimator using a ``init="random"`` and ``n_init=1``. This run leads to
-a bad convergence (local optimum) with estimated centers stuck
+a bad convergence (local optimum), with estimated centers stuck
 between ground truth clusters.
 
 The dataset used for evaluation is a 2D grid of isotropic Gaussian

dev/_downloads/4b8d8f0d50e5aa937ac9571a35eadc28/plot_kmeans_stability_low_dim_dense.py

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Empirical evaluation of the impact of k-means initialization\n\nEvaluate the ability of k-means initializations strategies to make\nthe algorithm convergence robust as measured by the relative standard\ndeviation of the inertia of the clustering (i.e. the sum of squared\ndistances to the nearest cluster center).\n\nThe first plot shows the best inertia reached for each combination\nof the model (``KMeans`` or ``MiniBatchKMeans``) and the init method\n(``init=\"random\"`` or ``init=\"kmeans++\"``) for increasing values of the\n``n_init`` parameter that controls the number of initializations.\n\nThe second plot demonstrate one single run of the ``MiniBatchKMeans``\nestimator using a ``init=\"random\"`` and ``n_init=1``. This run leads to\na bad convergence (local optimum) with estimated centers stuck\nbetween ground truth clusters.\n\nThe dataset used for evaluation is a 2D grid of isotropic Gaussian\nclusters widely spaced.\n"
+        "\n# Empirical evaluation of the impact of k-means initialization\n\nEvaluate the ability of k-means initializations strategies to make\nthe algorithm convergence robust, as measured by the relative standard\ndeviation of the inertia of the clustering (i.e. the sum of squared\ndistances to the nearest cluster center).\n\nThe first plot shows the best inertia reached for each combination\nof the model (``KMeans`` or ``MiniBatchKMeans``), and the init method\n(``init=\"random\"`` or ``init=\"kmeans++\"``) for increasing values of the\n``n_init`` parameter that controls the number of initializations.\n\nThe second plot demonstrates one single run of the ``MiniBatchKMeans``\nestimator using a ``init=\"random\"`` and ``n_init=1``. This run leads to\na bad convergence (local optimum), with estimated centers stuck\nbetween ground truth clusters.\n\nThe dataset used for evaluation is a 2D grid of isotropic Gaussian\nclusters widely spaced.\n"
       ]
     },
     {