43 lines
1.6 KiB
ReStructuredText
43 lines
1.6 KiB
ReStructuredText
.. _cross_decomposition:
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===================
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Cross decomposition
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===================
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.. currentmodule:: sklearn.cross_decomposition
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The cross decomposition module contains two main families of algorithms: the
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partial least squares (PLS) and the canonical correlation analysis (CCA).
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These families of algorithms are useful to find linear relations between two
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multivariate datasets: the ``X`` and ``Y`` arguments of the ``fit`` method
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are 2D arrays.
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.. figure:: ../auto_examples/cross_decomposition/images/sphx_glr_plot_compare_cross_decomposition_001.png
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:target: ../auto_examples/cross_decomposition/plot_compare_cross_decomposition.html
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:scale: 75%
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:align: center
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Cross decomposition algorithms find the fundamental relations between two
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matrices (X and Y). They are latent variable approaches to modeling the
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covariance structures in these two spaces. They will try to find the
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multidimensional direction in the X space that explains the maximum
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multidimensional variance direction in the Y space. PLS-regression is
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particularly suited when the matrix of predictors has more variables than
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observations, and when there is multicollinearity among X values. By contrast,
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standard regression will fail in these cases.
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Classes included in this module are :class:`PLSRegression`
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:class:`PLSCanonical`, :class:`CCA` and :class:`PLSSVD`
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.. topic:: Reference:
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* JA Wegelin
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`A survey of Partial Least Squares (PLS) methods, with emphasis on the two-block case <https://www.stat.washington.edu/research/reports/2000/tr371.pdf>`_
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.. topic:: Examples:
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* :ref:`sphx_glr_auto_examples_cross_decomposition_plot_compare_cross_decomposition.py`
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