8000 DOC Fix sphinx's links to Glossary (#21094) · scikit-learn/scikit-learn@01b9b5a · GitHub
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DOC Fix sphinx's links to Glossary (#21094)
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‎sklearn/covariance/_elliptic_envelope.py

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@@ -41,7 +41,7 @@ class EllipticEnvelope(OutlierMixin, MinCovDet):
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random_state : int, RandomState instance or None, default=None
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Determines the pseudo random number generator for shuffling
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the data. Pass an int for reproducible results across multiple function
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calls. See :term: `Glossary <random_state>`.
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calls. See :term:`Glossary <random_state>`.
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Attributes
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----------

‎sklearn/covariance/_robust_covariance.py

< 8000 /div>
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@@ -71,7 +71,7 @@ def c_step(
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random_state : int, RandomState instance or None, default=None
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Determines the pseudo random number generator for shuffling the data.
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Pass an int for reproducible results across multiple function calls.
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See :term: `Glossary <random_state>`.
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See :term:`Glossary <random_state>`.
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Returns
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-------
@@ -269,7 +269,7 @@ def select_candidates(
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random_state : int, RandomState instance or None, default=None
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Determines the pseudo random number generator for shuffling the data.
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Pass an int for reproducible results across multiple function calls.
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See :term: `Glossary <random_state>`.
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See :term:`Glossary <random_state>`.
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See Also
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---------
@@ -381,7 +381,7 @@ def fast_mcd(
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random_state : int, RandomState instance or None, default=None
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Determines the pseudo random number generator for shuffling the data.
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Pass an int for reproducible results across multiple function calls.
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See :term: `Glossary <random_state>`.
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See :term:`Glossary <random_state>`.
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Returns
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-------
@@ -610,7 +610,7 @@ class MinCovDet(EmpiricalCovariance):
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random_state : int, RandomState instance or None, default=None
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Determines the pseudo random number generator for shuffling the data.
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Pass an int for reproducible results across multiple function calls.
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See :term: `Glossary <random_state>`.
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See :term:`Glossary <random_state>`.
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Attributes
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----------

‎sklearn/ensemble/_hist_gradient_boosting/binning.py

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@@ -115,7 +115,7 @@ class _BinMapper(TransformerMixin, BaseEstimator):
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Pseudo-random number generator to control the random sub-sampling.
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Pass an int for reproducible output across multiple
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function calls.
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See :term: `Glossary <random_state>`.
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See :term:`Glossary <random_state>`.
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n_threads : int, default=None
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Number of OpenMP threads to use. `_openmp_effective_n_threads` is called
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to determine the effective number of threads use, which takes cgroups CPU

‎sklearn/gaussian_process/_gpc.py

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@@ -110,7 +110,7 @@ def optimizer(obj_func, initial_theta, bounds):
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random_state : int, RandomState instance or None, default=None
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Determines random number generation used to initialize the centers.
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Pass an int for reproducible results across multiple function calls.
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See :term: `Glossary <random_state>`.
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See :term:`Glossary <random_state>`.
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Attributes
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----------
@@ -559,7 +559,7 @@ def optimizer(obj_func, initial_theta, bounds):
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random_state : int, RandomState instance or None, default=None
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Determines random number generation used to initialize the centers.
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Pass an int for reproducible results across multiple function calls.
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See :term: `Glossary <random_state>`.
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See :term:`Glossary <random_state>`.
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multi_class : {'one_vs_rest', 'one_vs_one'}, default='one_vs_rest'
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Specifies how multi-class classification problems are handled.

‎sklearn/inspection/_permutation_importance.py

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@@ -171,7 +171,7 @@ def permutation_importance(
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Pseudo-random number generator to control the permutations of each
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feature.
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Pass an int to get reproducible results across function calls.
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See :term: `Glossary <random_state>`.
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See :term:`Glossary <random_state>`.
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sample_weight : array-like of shape (n_samples,), default=None
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Sample weights used in scoring.

‎sklearn/manifold/_locally_linear.py

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@@ -154,7 +154,7 @@ def null_space(
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random_state : int, RandomState instance, default=None
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Determines the random number generator when ``solver`` == 'arpack'.
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Pass an int for reproducible results across multiple function calls.
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See :term: `Glossary <random_state>`.
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See :term:`Glossary <random_state>`.
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"""
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if eigen_solver == "auto":
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if M.shape[0] > 200 and k + k_skip < 10:
@@ -268,7 +268,7 @@ def locally_linear_embedding(
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random_state : int, RandomState instance, default=None
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Determines the random number generator when ``solver`` == 'arpack'.
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Pass an int for reproducible results across multiple function calls.
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See :term: `Glossary <random_state>`.
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See :term:`Glossary <random_state>`.
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n_jobs : int or None, default=None
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The number of parallel jobs to run for neighbors search.
@@ -608,7 +608,7 @@ class LocallyLinearEmbedding(TransformerMixin, _UnstableArchMixin, BaseEstimator
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random_state : int, RandomState instance, default=None
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Determines the random number generator when
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``eigen_solver`` == 'arpack'. Pass an int for reproducible results
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across multiple function calls. See :term: `Glossary <random_state>`.
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across multiple function calls. See :term:`Glossary <random_state>`.
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n_jobs : int or None, default=None
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The number of parallel jobs to run.

‎sklearn/manifold/_mds.py

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@@ -61,7 +61,7 @@ def _smacof_single(
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random_state : int, RandomState instance or None, default=None
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Determines the random number generator used to initialize the centers.
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Pass an int for reproducible results across multiple function calls.
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See :term: `Glossary <random_state>`.
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See :term:`Glossary <random_state>`.
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Returns
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-------
@@ -220,7 +220,7 @@ def smacof(
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random_state : int, RandomState instance or None, default=None
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Determines the random number generator used to initialize the centers.
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Pass an int for reproducible results across multiple function calls.
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See :term: `Glossary <random_state>`.
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See :term:`Glossary <random_state>`.
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return_n_iter : bool, default=False
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Whether or not to return the number of iterations.
@@ -347,7 +347,7 @@ class MDS(BaseEstimator):
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random_state : int, RandomState instance or None, default=None
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Determines the random number generator used to initialize the centers.
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Pass an int for reproducible results across multiple function calls.
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See :term: `Glossary <random_state>`.
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See :term:`Glossary <random_state>`.
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dissimilarity : {'euclidean', 'precomputed'}, default='euclidean'
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Dissimilarity measure to use:

‎sklearn/manifold/_t_sne.py

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@@ -619,7 +619,7 @@ class TSNE(BaseEstimator):
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Determines the random number generator. Pass an int for reproducible
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results across multiple function calls. Note that different
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initializations might result in different local minima of the cost
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function. See :term: `Glossary <random_state>`.
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function. See :term:`Glossary <random_state>`.
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method : str, default='barnes_hut'
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By default the gradient calculation algorithm uses Barnes-Hut

‎sklearn/neighbors/_kde.py

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@@ -281,7 +281,7 @@ def sample(self, n_samples=1, random_state=None):
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Determines random number generation used to generate
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random samples. Pass an int for reproducible results
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across multiple function calls.
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See :term: `Glossary <random_state>`.
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See :term:`Glossary <random_state>`.
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Returns
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-------

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