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DOC fixes references issues given by sphinx (scikit-learn#27432)
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doc/modules/classes.rst

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@@ -10,6 +10,23 @@ function raw specifications may not be enough to give full guidelines on their
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uses.
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For reference on concepts repeated across the API, see :ref:`glossary`.
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:mod:`sklearn`: Settings and information tools
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==============================================
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.. automodule:: sklearn
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:no-members:
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:no-inherited-members:
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.. currentmodule:: sklearn
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.. autosummary::
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:toctree: generated/
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:template: function.rst
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config_context
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get_config
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set_config
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show_versions
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:mod:`sklearn.base`: Base classes and utility functions
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=======================================================
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base.clone
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base.is_classifier
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base.is_regressor
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config_context
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get_config
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set_config
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show_versions
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.. _calibration_ref:
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.. currentmodule:: sklearn
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.. autosummary::
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:toctree: generated
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:toctree: generated/
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:template: class.rst
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compose.ColumnTransformer
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.. currentmodule:: sklearn
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.. autosummary::
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:toctree: generated
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:toctree: generated/
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:template: class.rst
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discriminant_analysis.LinearDiscriminantAnalysis
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gaussian_process.GaussianProcessClassifier
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gaussian_process.GaussianProcessRegressor
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Kernels:
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Kernels
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-------
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.. automodule:: sklearn.gaussian_process.kernels
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:no-members:
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.. currentmodule:: sklearn
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isotonic.IsotonicRegression
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.. autosummary::
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:toctree: generated
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:toctree: generated/
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:template: function.rst
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isotonic.check_increasing
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.. autosummary::
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:toctree: generated/
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:template: function.rst
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:template: classes.rst
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linear_model.PassiveAggressiveRegressor
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.. autosummary::
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:toctree: generated/
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:template: function.rst
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linear_model.enet_path
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linear_model.lars_path
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linear_model.lars_path_gram
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.. autosummary::
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:toctree: generated
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:toctree: generated/
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:template: class.rst
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multiclass.OneVsRestClassifier
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:toctree: generated/
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:template: function.rst
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utils.arrayfuncs.min_pos
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utils.as_float_array
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utils.assert_all_finite
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utils.deprecated
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utils.estimator_html_repr
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utils.gen_batches
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utils.gen_even_slices
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utils.indexable
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utils.murmurhash3_32
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utils.resample
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utils._safe_indexing
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utils.safe_mask
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utils.safe_sqr
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utils.shuffle
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Input and parameter validation
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------------------------------
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.. automodule:: sklearn.utils.validation
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:no-members:
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:template: function.rst
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utils.check_X_y
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utils.check_array
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utils.check_scalar
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utils.check_consistent_length
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utils.check_random_state
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utils.validation.check_is_fitted
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utils.validation.check_memory
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utils.validation.check_symmetric
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utils.validation.column_or_1d
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utils.validation.has_fit_parameter
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Utilities used in meta-estimators
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---------------------------------
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.. automodule:: sklearn.utils.metaestimators
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:toctree: generated/
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:template: function.rst
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utils.metaestimators.available_if
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Utilities to handle weights based on class labels
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-------------------------------------------------
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.. automodule:: sklearn.utils.class_weight
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:no-members:
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.. autosummary::
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:template: function.rst
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utils.class_weight.compute_class_weight
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utils.class_weight.compute_sample_weight
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utils.deprecated
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utils.estimator_checks.check_estimator
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utils.estimator_checks.parametrize_with_checks
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utils.estimator_html_repr
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Utilities to deal with multiclass target in classifiers
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-------------------------------------------------------
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.. automodule:: sklearn.utils.multiclass
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:no-members:
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.. autosummary::
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:template: function.rst
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utils.multiclass.type_of_target
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utils.multiclass.is_multilabel
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utils.multiclass.unique_labels
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Utilities for optimal mathematical operations
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---------------------------------------------
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.. automodule:: sklearn.utils.extmath
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:no-members:
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.. autosummary::
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:template: function.rst
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utils.extmath.safe_sparse_dot
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utils.extmath.randomized_range_finder
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utils.extmath.randomized_svd
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utils.extmath.fast_logdet
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utils.extmath.density
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utils.extmath.weighted_mode
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utils.gen_batches
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utils.gen_even_slices
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utils.graph.single_source_shortest_path_length
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utils.indexable
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utils.metaestimators.available_if
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utils.multiclass.type_of_target
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utils.multiclass.is_multilabel
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utils.multiclass.unique_labels
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utils.murmurhash3_32
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utils.resample
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utils._safe_indexing
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utils.safe_mask
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utils.safe_sqr
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utils.shuffle
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Utilities to work with sparse matrices and arrays
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-------------------------------------------------
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.. automodule:: sklearn.utils.sparsefuncs
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utils.sparsefuncs.inplace_row_scale
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utils.sparsefuncs.inplace_csr_column_scale
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.. automodule:: sklearn.utils.sparsefuncs_fast
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Utilities to work with graphs
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-----------------------------
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.. automodule:: sklearn.utils.graph
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Utilities for random sampling
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-----------------------------
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Utilities to operate on arrays
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------------------------------
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Metadata routing
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----------------
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.. automodule:: sklearn.utils.metadata_routing
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utils.metadata_routing.process_routing
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Scikit-learn object discovery
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-----------------------------
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.. automodule:: sklearn.utils.discovery
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Utilities from joblib:
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Scikit-learn compatibility checker
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----------------------------------
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.. automodule:: sklearn.utils.estimator_checks
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Utilities for parallel computing
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--------------------------------
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