Pushing the docs to dev/ for branch: main, commit 768c01d0c625549681609371381bf5dfda51ed81

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -105,7 +105,7 @@
 X, y = make_blobs(random_state=rng)
 X = scipy.sparse.csr_matrix(X)
 X_train, X_test, _, y_test = train_test_split(X, y, random_state=rng)
-kmeans = KMeans(algorithm="elkan").fit(X_train)
+kmeans = KMeans(n_init="auto").fit(X_train)
 print(completeness_score(kmeans.predict(X_test), y_test))
 
 ##############################################################################

dev/_downloads/4f07b03421908788913e044918d8ed1e/plot_release_highlights_0_23_0.py

@@ -69,7 +69,7 @@
       },
       "outputs": [],
       "source": [
-        "import scipy\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.cluster import KMeans\nfrom sklearn.datasets import make_blobs\nfrom sklearn.metrics import completeness_score\n\nrng = np.random.RandomState(0)\nX, y = make_blobs(random_state=rng)\nX = scipy.sparse.csr_matrix(X)\nX_train, X_test, _, y_test = train_test_split(X, y, random_state=rng)\nkmeans = KMeans(algorithm=\"elkan\").fit(X_train)\nprint(completeness_score(kmeans.predict(X_test), y_test))"
+        "import scipy\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.cluster import KMeans\nfrom sklearn.datasets import make_blobs\nfrom sklearn.metrics import completeness_score\n\nrng = np.random.RandomState(0)\nX, y = make_blobs(random_state=rng)\nX = scipy.sparse.csr_matrix(X)\nX_train, X_test, _, y_test = train_test_split(X, y, random_state=rng)\nkmeans = KMeans(n_init=\"auto\").fit(X_train)\nprint(completeness_score(kmeans.predict(X_test), y_test))"
       ]
     },
     {