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MNT Fix rst issues found by sphinx-lint (#31114)
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‎doc/developers/contributing.rst

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@@ -292,10 +292,10 @@ how to set up your git repository:
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.. code-block:: text
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origin git@github.com:YourLogin/scikit-learn.git (fetch)
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origin git@github.com:YourLogin/scikit-learn.git (push)
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upstream git@github.com:scikit-learn/scikit-learn.git (fetch)
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upstream git@github.com:scikit-learn/scikit-learn.git (push)
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origin git@github.com:YourLogin/scikit-learn.git (fetch)
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origin git@github.com:YourLogin/scikit-learn.git (push)
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upstream git@github.com:scikit-learn/scikit-learn.git (fetch)
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upstream git@github.com:scikit-learn/scikit-learn.git (push)
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You should now have a working installation of scikit-learn, and your git repository
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properly configured. It could be useful to run some test to verify your installation.

‎doc/developers/develop.rst

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@@ -499,7 +499,7 @@ Estimator Tags
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The estimator tags are annotations of estimators that allow programmatic inspection of
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their capabilities, such as sparse matrix support, supported output types and supported
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methods. The estimator tags are an instance of :class:`~sklearn.utils.Tags` returned by
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the method :meth:`~sklearn.base.BaseEstimator.__sklearn_tags__()`. These tags are used
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the method :meth:`~sklearn.base.BaseEstimator.__sklearn_tags__`. These tags are used
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in different places, such as :func:`~base.is_regressor` or the common checks run by
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:func:`~sklearn.utils.estimator_checks.check_estimator` and
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:func:`~sklearn.utils.estimator_checks.parametrize_with_checks`, where tags determine

‎doc/modules/cross_validation.rst

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===================
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If the data ordering is not arbitrary (e.g. samples with the same class label
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are contiguous), shuffling it first may be essential to get a meaningful cross-
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validation result. However, the opposite may be true if the samples are not
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are contiguous), shuffling it first may be essential to get a meaningful
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cross-validation result. However, the opposite may be true if the samples are not
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independently and identically distributed. For example, if samples correspond
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to news articles, and are ordered by their time of publication, then shuffling
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the data will likely lead to a model that is overfit and an inflated validation
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to shuffle the data indices before splitting them. Note that:
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* This consumes less memory than shuffling the data directly.
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* By default no shuffling occurs, including for the (stratified) K fold cross-
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validation performed by specifying ``cv=some_integer`` to
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* By default no shuffling occurs, including for the (stratified) K fold
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cross-validation performed by specifying ``cv=some_integer`` to
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:func:`cross_val_score`, grid search, etc. Keep in mind that
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:func:`train_test_split` still returns a random split.
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* The ``random_state`` parameter defaults to ``None``, meaning that the

‎doc/modules/ensemble.rst

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================ ========== ========== ==========
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classifier class 1 class 2 class 3
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================ ========== ========== ==========
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classifier 1 w1 * 0.2 w1 * 0.5 w1 * 0.3
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classifier 2 w2 * 0.6 w2 * 0.3 w2 * 0.1
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classifier 1 w1 * 0.2 w1 * 0.5 w1 * 0.3
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classifier 2 w2 * 0.6 w2 * 0.3 w2 * 0.1
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classifier 3 w3 * 0.3 w3 * 0.4 w3 * 0.3
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weighted average 0.37 0.4 0.23
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weighted average 0.37 0.4 0.23
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================ ========== ========== ==========
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Here, the predicted class label is 2, since it has the highest average probability. See

‎doc/modules/feature_extraction.rst

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Vectorizing a large text corpus with the hashing trick
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------------------------------------------------------
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The above vectorization scheme is simple but the fact that it holds an **in-
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memory mapping from the string tokens to the integer feature indices** (the
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``vocabulary_`` attribute) causes several **problems when dealing with large
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The above vectorization scheme is simple but the fact that it holds an
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**in-memory mapping from the string tokens to the integer feature indices**
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(the ``vocabulary_`` attribute) causes several **problems when dealing with large
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datasets**:
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- the larger the corpus, the larger the vocabulary will grow and hence the

‎doc/modules/lda_qda.rst

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.. note:: **Relation with Gaussian Naive Bayes**
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If in the QDA model one assumes that the covariance matrices are diagonal,
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then the inputs are assumed to be conditionally independent in each class,
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and the resulting classifier is equivalent to the Gaussian Naive Bayes
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classifier :class:`naive_bayes.GaussianNB`.
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If in the QDA model one assumes that the covariance matrices are diagonal,
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then the inputs are assumed to be conditionally independent in each class,
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and the resulting classifier is equivalent to the Gaussian Naive Bayes
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classifier :class:`naive_bayes.GaussianNB`.
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LDA
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---

‎doc/modules/model_evaluation.rst

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'neg_mean_poisson_deviance' :func:`metrics.mean_poisson_deviance`
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'neg_mean_gamma_deviance' :func:`metrics.mean_gamma_deviance`
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'neg_mean_absolute_percentage_error' :func:`metrics.mean_absolute_percentage_error`
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'd2_absolute_error_score' :func:`metrics.d2_absolute_error_score`
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'd2_absolute_error_score' :func:`metrics.d2_absolute_error_score`
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==================================== ============================================== ==================================
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Usage examples:

‎doc/modules/multiclass.rst

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>>> y_sparse = sparse.csr_matrix(y_dense)
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>>> print(y_sparse)
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<Compressed Sparse Row sparse matrix of dtype 'int64'
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with 4 stored elements and shape (4, 3)>
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Coords Values
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(0, 0) 1
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(1, 2) 1
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(2, 0) 1
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(3, 1) 1
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with 4 stored elements and shape (4, 3)>
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Coords Values
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(0, 0) 1
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(1, 2) 1
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(2, 0) 1
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(3, 1) 1
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For more information about :class:`~sklearn.preprocessing.LabelBinarizer`,
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refer to :ref:`preprocessing_targets`.
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>>> print(y_sparse)
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<Compressed Sparse Row sparse matrix of dtype 'int64'
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with 4 stored elements and shape (3, 4)>
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Coords Values
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(0, 0) 1
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(0, 3) 1
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(1, 2) 1
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(1, 3) 1
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Coords Values
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(0, 0) 1
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(0, 3) 1
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(1, 2) 1
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(1, 3) 1
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.. _multioutputclassfier:
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‎doc/related_projects.rst

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- `dtreeviz <https://github.com/parrt/dtreeviz/>`_ A Python library for
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decision tree visualization and model interpretation.
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- `model-diagnostics <https://lorentzenchr.github.io/model-diagnostics/>` Tools for
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- `model-diagnostics <https://lorentzenchr.github.io/model-diagnostics/>`_ Tools for
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diagnostics and assessment of (machine learning) models (in Python).
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- `sklearn-evaluation <https://github.com/ploomber/sklearn-evaluation>`_

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