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DOC Fix warnings about references and links (#14976)
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‎doc/glossary.rst

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picklable. This means, for instance, that lambdas cannot be used
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as estimator parameters.
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``pos_label``
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Value with which positive labels must be encoded in binary
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classification problems in which the positive class is not assumed.
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This value is typically required to compute asymmetric evaluation
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metrics such as precision and recall.
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``random_state``
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Whenever randomization is part of a Scikit-learn algorithm, a
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``random_state`` parameter may be provided to control the random number

‎doc/modules/computing.rst

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:SKLEARN_WORKING_MEMORY:
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Sets the default value for the :term:`working_memory` argument of
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Sets the default value for the `working_memory` argument of
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:func:`sklearn.set_config`.
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:SKLEARN_SEED:

‎doc/modules/ensemble.rst

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Scikit-learn 0.21 introduces two new experimental implementations of
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gradient boosting trees, namely :class:`HistGradientBoostingClassifier`
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and :class:`HistGradientBoostingRegressor`, inspired by
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`LightGBM <https://github.com/Microsoft/LightGBM>`__.
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`LightGBM <https://github.com/Microsoft/LightGBM>`__ (See [LightGBM]_).
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These histogram-based estimators can be **orders of magnitude faster**
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than :class:`GradientBoostingClassifier` and
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Scikit-learn 0.21 introduces two new experimental implementations of
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gradient boosting trees, namely :class:`HistGradientBoostingClassifier`
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and :class:`HistGradientBoostingRegressor`, inspired by
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`LightGBM <https://github.com/Microsoft/LightGBM>`__.
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`LightGBM <https://github.com/Microsoft/LightGBM>`__ (See [LightGBM]_).
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These histogram-based estimators can be **orders of magnitude faster**
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than :class:`GradientBoostingClassifier` and
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.. topic:: References
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.. [XGBoost] Tianqi Chen, Carlos Guestrin, "XGBoost: A Scalable Tree
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Boosting System". https://arxiv.org/abs/1603.02754
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.. [LightGBM] Ke et. al. "LightGBM: A Highly Efficient Gradient
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BoostingDecision Tree"
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.. [F1999] Friedmann, Jerome H., 2007, `"Stochastic Gradient Boosting"
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<https://statweb.stanford.edu/~jhf/ftp/stobst.pdf>`_
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.. [R2007] G. Ridgeway, "Generalized Boosted Models: A guide to the gbm
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package", 2007
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.. [XGBoost] Tianqi Chen, Carlos Guestrin, `"XGBoost: A Scalable Tree
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Boosting System" <https://arxiv.org/abs/1603.02754>`_
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.. [LightGBM] Ke et. al. `"LightGBM: A Highly Efficient Gradient
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BoostingDecision Tree" <https://papers.nips.cc/paper/
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6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree>`_
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.. _voting_classifier:
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‎doc/modules/neighbors.rst

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J. Goldberger, S. Roweis, G. Hinton, R. Salakhutdinov, Advances in
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Neural Information Processing Systems, Vol. 17, May 2005, pp. 513-520.
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.. [2] `Wikipedia entry on Neighborhood Components Analysis
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<https://en.wikipedia.org/wiki/Neighbourhood_components_analysis>`_
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`Wikipedia entry on Neighborhood Components Analysis
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<https://en.wikipedia.org/wiki/Neighbourhood_components_analysis>`_

‎doc/modules/partial_dependence.rst

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Statistical Learning <https://web.stanford.edu/~hastie/ElemStatLearn//>`_,
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Second Edition, Section 10.13.2, Springer, 2009.
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.. [Mol2019] C. Molnar, `Interpretable Machine Learning
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C. Molnar, `Interpretable Machine Learning
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<https://christophm.github.io/interpretable-ml-book/>`_, Section 5.1, 2019.

‎examples/decomposition/plot_faces_decomposition.py

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Faces dataset decompositions
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============================
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This example applies to :ref:`olivetti_faces` different unsupervised
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This example applies to :ref:`olivetti_faces_dataset` different unsupervised
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matrix decomposition (dimension reduction) methods from the module
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:py:mod:`sklearn.decomposition` (see the documentation chapter
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:ref:`decompositions`) .

‎examples/inspection/plot_permutation_importance.py

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2020
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.. topic:: References:
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.. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32,
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[1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32,
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2001. https://doi.org/10.1023/A:1010933404324
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"""
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print(__doc__)

‎examples/multioutput/plot_classifier_chain_yeast.py

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data point has at least one label. As a baseline we first train a logistic
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regression classifier for each of the 14 labels. To evaluate the performance of
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these classifiers we predict on a held-out test set and calculate the
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:ref:`jaccard score <jaccard_score>` for each sample.
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:ref:`jaccard score <jaccard_similarity_score>` for each sample.
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Next we create 10 classifier chains. Each classifier chain contains a
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logistic regression model for each of the 14 labels. The models in each

‎sklearn/decomposition/online_lda.py

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References
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----------
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[1] "Online Learning for Latent Dirichlet Allocation", Matthew D. Hoffman,
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David M. Blei, Francis Bach, 2010
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.. [1] "Online Learning for Latent Dirichlet Allocation", Matthew D.
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Hoffman, David M. Blei, Francis Bach, 2010
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[2] "Stochastic Variational Inference", Matthew D. Hoffman, David M. Blei,
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Chong Wang, John Paisley, 2013

‎sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py

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n_trees_per_iteration_ : int
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The number of tree that are built at each iteration. For regressors,
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this is always 1.
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train_score_ : ndarray, shape (n_iter_ + 1,)
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train_score_ : ndarray, shape (n_iter_+1,)
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The scores at each iteration on the training data. The first entry
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is the score of the ensemble before the first iteration. Scores are
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computed according to the ``scoring`` parameter. If ``scoring`` is
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not 'loss', scores are computed on a subset of at most 10 000
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samples. Empty if no early stopping.
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validation_score_ : ndarray, shape (n_iter_ + 1,)
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validation_score_ : ndarray, shape (n_iter_+1,)
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The scores at each iteration on the held-out validation data. The
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first entry is the score of the ensemble before the first iteration.
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Scores are computed according to the ``scoring`` parameter. Empty if
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The number of tree that are built at each iteration. This is equal to 1
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for binary classification, and to ``n_classes`` for multiclass
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classification.
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train_score_ : ndarray, shape (n_iter_ + 1,)
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train_score_ : ndarray, shape (n_iter_+1,)
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The scores at each iteration on the training data. The first entry
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is the score of the ensemble before the first iteration. Scores are
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computed according to the ``scoring`` parameter. If ``scoring`` is
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not 'loss', scores are computed on a subset of at most 10 000
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samples. Empty if no early stopping.
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validation_score_ : ndarray, shape (n_iter_ + 1,)
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validation_score_ : ndarray, shape (n_iter_+1,)
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The scores at each iteration on the held-out validation data. The
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first entry is the score of the ensemble before the first iteration.
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Scores are computed according to the ``scoring`` parameter. Empty if

‎sklearn/ensemble/partial_dependence.py

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Dict with keywords passed to the ``matplotlib.pyplot.plot`` call.
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For two-way partial dependence plots.
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**fig_kw : dict
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``**fig_kw`` : dict
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Dict with keywords passed to the figure() call.
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Note that all keywords not recognized above will be automatically
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included here.

‎sklearn/impute/_knn.py

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- 'nan_euclidean'
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- callable : a user-defined function which conforms to the definition
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of _pairwise_callable(X, Y, metric, **kwds). The function
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of ``_pairwise_callable(X, Y, metric, **kwds)``. The function
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accepts two arrays, X and Y, and a `missing_values` keyword in
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`kwds` and returns a scalar distance value.
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‎sklearn/linear_model/bayes.py

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sigma_ : array-like of shape (n_features, n_features)
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Estimated variance-covariance matrix of the weights
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scores_ : array-like of shape (n_iter_ + 1,)
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scores_ : array-like of shape (n_iter_+1,)
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If computed_score is True, value of the log marginal likelihood (to be
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maximized) at each iteration of the optimization. The array starts
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with the value of the log marginal likelihood obtained for the initial

‎sklearn/metrics/ranking.py

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References
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----------
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`Wikipedia entry for Discounted Cumulative Gain
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<https://en.wikipedia.org/wiki/Discounted_cumulative_gain>`_
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<https://en.wikipedia.org/wiki/Discounted_cumulative_gain>`_
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Jarvelin, K., & Kekalainen, J. (2002).
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Cumulated gain-based evaluation of IR techniques. ACM Transactions on
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References
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`Wikipedia entry for Discounted Cumulative Gain
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<https://en.wikipedia.org/wiki/Discounted_cumulative_gain>`_
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<https://en.wikipedia.org/wiki/Discounted_cumulative_gain>`_
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Jarvelin, K., & Kekalainen, J. (2002).
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Cumulated gain-based evaluation of IR techniques. ACM Transactions on

‎sklearn/svm/classes.py

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Scalable linear Support Vector Machine for classification using
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liblinear.
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Notes
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-----
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**References:** 80D9
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`LIBSVM: A Library for Support Vector Machines
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<http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf>`__
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References
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----------
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.. [1] `LIBSVM: A Library for Support Vector Machines
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<http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf>`_
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.. [2] `Platt, John (1999). "Probabilistic outputs for support vector
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machines and comparison to regularizedlikelihood methods."
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<http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.1639>`_
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"""
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_impl = 'nu_svc'

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