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DOC subdivide documentation on sample generators
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doc/datasets/index.rst

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In addition, scikit-learn includes various random sample generators that
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can be used to build artificial datasets of controlled size and complexity.
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Generators for classification and clustering
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--------------------------------------------
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.. image:: ../auto_examples/datasets/images/plot_random_dataset_1.png
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:target: ../auto_examples/datasets/plot_random_dataset.html
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:scale: 50
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:align: center
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Multiclass target
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~~~~~~~~~~~~~~~~~
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.. autosummary::
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:toctree: ../modules/generated/
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:template: function.rst
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make_classification
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make_blobs
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make_gaussian_quantiles
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Binary target
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~~~~~~~~~~~~~
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.. autosummary::
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:toctree: ../modules/generated/
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:template: function.rst
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make_hastie_10_2
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make_circles
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make_moons
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Multilabel
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~~~~~~~~~~
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.. autosummary::
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:toctree: ../modules/generated/
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:template: function.rst
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make_multilabel_classification
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Biclustering
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~~~~~~~~~~~~
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.. autosummary::
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:toctree: ../modules/generated/
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:template: function.rst
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make_biclusters
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make_checkerboard
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Generators for regression
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-------------------------
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.. autosummary::
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:toctree: ../modules/generated/
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:template: function.rst
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make_regression
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make_blobs
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make_friedman1
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make_friedman2
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make_friedman3
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make_hastie_10_2
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make_sparse_uncorrelated
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Generators for decomposition
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----------------------------
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.. autosummary::
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:toctree: ../modules/generated/
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:template: function.rst
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make_low_rank_matrix
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make_sparse_coded_signal
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make_sparse_uncorrelated
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make_spd_matrix
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make_swiss_roll
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make_s_curve
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make_sparse_spd_matrix
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make_biclusters
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make_checkerboard
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.. _libsvm_loader:
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