41 lines
1.3 KiB
ReStructuredText
41 lines
1.3 KiB
ReStructuredText
.. _pls:
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======================
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Partial Least Squares
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======================
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.. currentmodule:: sklearn.pls
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Partial least squares (PLS) models are useful to find linear relations
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between two multivariate datasets: in PLS the `X` and `Y` arguments of
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the `fit` method are 2D arrays.
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.. figure:: ../auto_examples/images/plot_pls_1.png
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:target: ../auto_examples/plot_pls.html
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:scale: 75%
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:align: center
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PLS finds the fundamental relations between two matrices
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(X and Y): it is a latent variable approach to modeling the covariance
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structures in these two spaces. A PLS model 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
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than observations, and when there is multicollinearity among X
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values. By contrast, 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/www/research/reports/2000/tr371.pdf>`_
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.. topic:: Examples:
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* :ref:`example_plot_pls.py`
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