Pushing the docs to dev/ for branch: main, commit 286f0c9d17019e52f532d63b5ace9f8e1beb5fe5

Author: sklearn-ci

Diff for: 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: 4a48988f39733080cfcf264cdeeac647
+config: ddc4265868146c3ec51481a0465eb13f
 tags: 645f666f9bcd5a90fca523b33c5a78b7

Diff for: dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -47,7 +47,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Plot ROC and DET curves\n\nDET curves are commonly plotted in normal deviate scale. To achieve this the\nDET display transforms the error rates as returned by the\n:func:`~sklearn.metrics.det_curve` and the axis scale using\n:func:`scipy.stats.norm`.\n\n"
+        "## Plot ROC and DET curves\n\nDET curves are commonly plotted in normal deviate scale. To achieve this the\nDET display transforms the error rates as returned by the\n:func:`~sklearn.metrics.det_curve` and the axis scale using\n`scipy.stats.norm`.\n\n"
       ]
     },
     {

Diff for: dev/_downloads/10bb40e21b74618cdeed618ff1eae595/plot_det.ipynb

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# `__sklearn_is_fitted__` as Developer API\n\nThe `__sklearn_is_fitted__` method is a convention used in scikit-learn for\nchecking whether an estimator object has been fitted or not. This method is\ntypically implemented in custom estimator classes that are built on top of\nscikit-learn's base classes like `BaseEstimator` or its subclasses.\n\nDevelopers should use :func:`~sklearn.sklearn.utils.validation.check_is_fitted`\nat the beginning of all methods except `fit`. If they need to customize or\nspeed-up the check, they can implement the `__sklearn_is_fitted__` method as\nshown below.\n\nIn this example the custom estimator showcases the usage of the\n`__sklearn_is_fitted__` method and the `check_is_fitted` utility function\nas developer APIs. The `__sklearn_is_fitted__` method checks fitted status\nby verifying the presence of the `_is_fitted` attribute.\n"
+        "\n# `__sklearn_is_fitted__` as Developer API\n\nThe `__sklearn_is_fitted__` method is a convention used in scikit-learn for\nchecking whether an estimator object has been fitted or not. This method is\ntypically implemented in custom estimator classes that are built on top of\nscikit-learn's base classes like `BaseEstimator` or its subclasses.\n\nDevelopers should use :func:`~sklearn.utils.validation.check_is_fitted`\nat the beginning of all methods except `fit`. If they need to customize or\nspeed-up the check, they can implement the `__sklearn_is_fitted__` method as\nshown below.\n\nIn this example the custom estimator showcases the usage of the\n`__sklearn_is_fitted__` method and the `check_is_fitted` utility function\nas developer APIs. The `__sklearn_is_fitted__` method checks fitted status\nby verifying the presence of the `_is_fitted` attribute.\n"
       ]
     },
     {

Diff for: dev/_downloads/1ba22806e611e4b95c469e74c4b23796/sklearn_is_fitted.ipynb

@@ -182,7 +182,7 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray):
 #
 # :class:`sklearn.cluster.MiniBatchKMeans` is computationally efficient and
 # implements on-line learning with a
-# :meth:`~sklearn.decomposition.MiniBatchKMeans.partial_fit` method. That is
+# :meth:`~sklearn.cluster.MiniBatchKMeans.partial_fit` method. That is
 # why it could be beneficial to enhance some time-consuming algorithms with
 # :class:`~sklearn.cluster.MiniBatchKMeans`.
 

Diff for: dev/_downloads/4825fc8223d1af0f3b61080c3dea3a62/plot_faces_decomposition.py

@@ -79,7 +79,7 @@
 # DET curves are commonly plotted in normal deviate scale. To achieve this the
 # DET display transforms the error rates as returned by the
 # :func:`~sklearn.metrics.det_curve` and the axis scale using
-# :func:`scipy.stats.norm`.
+# `scipy.stats.norm`.
 
 import matplotlib.pyplot as plt
 

Diff for: dev/_downloads/67703ae8c65716668dd87c31a24a069b/plot_det.py

@@ -8,7 +8,7 @@
 typically implemented in custom estimator classes that are built on top of
 scikit-learn's base classes like `BaseEstimator` or its subclasses.
 
-Developers should use :func:`~sklearn.sklearn.utils.validation.check_is_fitted`
+Developers should use :func:`~sklearn.utils.validation.check_is_fitted`
 at the beginning of all methods except `fit`. If they need to customize or
 speed-up the check, they can implement the `__sklearn_is_fitted__` method as
 shown below.