60 lines
1.9 KiB
ReStructuredText
60 lines
1.9 KiB
ReStructuredText
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.. _data_reduction:
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=====================================
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Unsupervised dimensionality reduction
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=====================================
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If your number of features is high, it may be useful to reduce it with an
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unsupervised step prior to supervised steps. Many of the
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:ref:`unsupervised-learning` methods implement a ``transform`` method that
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can be used to reduce the dimensionality. Below we discuss two specific
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example of this pattern that are heavily used.
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.. topic:: **Pipelining**
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The unsupervised data reduction and the supervised estimator can be
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chained in one step. See :ref:`pipeline`.
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.. currentmodule:: sklearn
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PCA: principal component analysis
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----------------------------------
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:class:`decomposition.PCA` looks for a combination of features that
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capture well the variance of the original features. See :ref:`decompositions`.
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.. rubric:: Examples
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* :ref:`sphx_glr_auto_examples_applications_plot_face_recognition.py`
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Random projections
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-------------------
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The module: :mod:`~sklearn.random_projection` provides several tools for data
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reduction by random projections. See the relevant section of the
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documentation: :ref:`random_projection`.
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.. rubric:: Examples
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* :ref:`sphx_glr_auto_examples_miscellaneous_plot_johnson_lindenstrauss_bound.py`
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Feature agglomeration
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------------------------
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:class:`cluster.FeatureAgglomeration` applies
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:ref:`hierarchical_clustering` to group together features that behave
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similarly.
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.. rubric:: Examples
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* :ref:`sphx_glr_auto_examples_cluster_plot_feature_agglomeration_vs_univariate_selection.py`
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* :ref:`sphx_glr_auto_examples_cluster_plot_digits_agglomeration.py`
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.. topic:: **Feature scaling**
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Note that if features have very different scaling or statistical
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properties, :class:`cluster.FeatureAgglomeration` may not be able to
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capture the links between related features. Using a
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:class:`preprocessing.StandardScaler` can be useful in these settings.
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