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DOC adding backlinks to docstrings
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‎doc/modules/clustering.rst‎

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.. currentmodule:: sklearn.metrics
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.. _adjusted_rand_score:
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Adjusted Rand index
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-------------------
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Presentation and usage
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~~~~~~~~~~~~~~~~~~~~~~
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Given the knowledge of the ground truth class assignments ``labels_true``
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and our clustering algorithm assignments of the same samples
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``labels_pred``, the **adjusted Rand index** is a function that measures
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* `Wikipedia entry for the adjusted Rand index
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<http://en.wikipedia.org/wiki/Rand_index#Adjusted_Rand_index>`_
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.. _mutual_info_score:
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Mutual Information based scores
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-------------------------------
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Presentation and usage
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~~~~~~~~~~~~~~~~~~~~~~
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Given the knowledge of the ground truth class assignments ``labels_true`` and
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our clustering algorithm assignments of the same samples ``labels_pred``, the
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**Mutual Information** is a function that measures the **agreement** of the two
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* `Wikipedia entry for the Adjusted Mutual Information
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<http://en.wikipedia.org/wiki/Adjusted_Mutual_Information>`_
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.. _homogeneity_completeness:
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Homogeneity, completeness and V-measure
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---------------------------------------
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Presentation and usage
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~~~~~~~~~~~~~~~~~~~~~~
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Given the knowledge of the ground truth class assignments of the samples,
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it is possible to define some intuitive metric using conditional entropy
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analysis.
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Silhouette Coefficient
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----------------------
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Presentation and usage
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~~~~~~~~~~~~~~~~~~~~~~
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If the ground truth labels are not known, evaluation must be performed using
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the model itself. The Silhouette Coefficient
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(:func:`sklearn.metrics.silhouette_score`)

‎doc/modules/covariance.rst‎

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@@ -248,6 +248,7 @@ paper. It is the same algorithm as in the R ``glasso`` package.
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graphical lasso" <http://biostatistics.oxfordjournals.org/content/9/3/432.short>`_,
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Biostatistics 9, pp 432, 2008
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.. _robust_covariance:
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Robust Covariance Estimation
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============================

‎doc/modules/decomposition.rst‎

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* :math:`\Psi = \sigma^2 \mathbf{I}`: This assumption leads to
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the probabilistic model of :class:`PCA`.
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* :math:`\Psi = diag(\psi_1, \psi_2, \dots, \psi_n)`: This model is called Factor
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Analysis, a classical statistical model. The matrix W is sometimes called
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the "factor loading matrix".
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* :math:`\Psi = diag(\psi_1, \psi_2, \dots, \psi_n)`: This model is called
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:class:`FactorAnalysis`, a classical statistical model. The matrix W is
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sometimes called the "factor loading matrix".
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Both model essentially estimate a Gaussian with a low-rank covariance matrix.
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Because both models are probabilistic they can be integrated in more complex

‎doc/modules/ensemble.rst‎

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.. currentmodule:: sklearn.ensemble.partial_dependence
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.. _partial_dependence:
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Partial dependence
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..................
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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 0.3
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weighted average 0.37 0.4 0.3
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================ ========== ========== ==========
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Here, the predicted class label is 2, since it has the
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:scale: 75%
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Using the `VotingClassifier` with `GridSearch`
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---------------------------------------------
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----------------------------------------------
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The `VotingClassifier` can also be used together with `GridSearch` in order
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to tune the hyperparameters of the individual estimators::

‎doc/modules/feature_extraction.rst‎

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Customizing the vectorizer can also be useful when handling Asian languages
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that do not use an explicit word separator such as whitespace.
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.. _image_feature_extraction:
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Image feature extraction
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========================

‎doc/modules/feature_selection.rst‎

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high-dimensional datasets.
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.. _variance_threshold:
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Removing features with low variance
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===================================
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As expected, ``VarianceThreshold`` has removed the first column,
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which has a probability :math:`p = 5/6 > .8` of containing a zero.
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.. _univariate_feature_selection:
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Univariate feature selection
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============================
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:ref:`example_feature_selection_plot_feature_selection.py`
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.. _rfe:
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Recursive feature elimination
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=============================

‎doc/modules/grid_search.rst‎

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classifier (here a linear SVM trained with SGD with either elastic
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net or L2 penalty) using a :class:`pipeline.Pipeline` instance.
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.. _randomized_parameter_search:
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Randomized Parameter Optimization
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=================================
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While using a grid of parameter settings is currently the most widely used

‎doc/modules/kernel_approximation.rst‎

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The number of samples used - which is also the dimensionality of the features computed -
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.. _rbf_kernel_approx:
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Radial Basis Function Kernel
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* :ref:`example_plot_kernel_approximation.py`
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.. _additive_chi_kernel_approx:
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Additive Chi Squared Kernel
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See the [VZ2010]_ for details and [VVZ2010]_ for combination with the :class:`RBFSampler`.
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.. _skewed_chi_kernel_approx:
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Skewed Chi Squared Kernel
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-------------------------

‎doc/modules/linear_model.rst‎

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* :ref:`example_linear_model_plot_lasso_model_selection.py`
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.. _elastic_net:
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Elastic Net
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<http://blanche.polytechnique.fr/~mallat/papiers/MallatPursuit93.pdf>`_,
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S. G. Mallat, Z. Zhang,
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.. _bayesian_regression:
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Bayesian Regression
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===================
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* :ref:`sgd`
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.. _perceptron:
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Perceptron
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==========
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‎doc/modules/manifold.rst‎

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<http://www.sciencemag.org/content/290/5500/2319.full>`_
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Tenenbaum, J.B.; De Silva, V.; & Langford, J.C. Science 290 (5500)
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.. _locally_linear_embedding:
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Locally Linear Embedding
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========================

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