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.. _pls:
======================
Partial Least Squares
======================
.. currentmodule:: sklearn.pls
Partial least squares (PLS) models are useful to find linear relations
between two multivariate datasets: in PLS the `X` and `Y` arguments of
the `fit` method are 2D arrays.
.. figure:: ../auto_examples/images/plot_pls_1.png
:target: ../auto_examples/plot_pls.html
:scale: 75%
:align: center
PLS finds the fundamental relations between two matrices
(X and Y): it is a latent variable approach to modeling the covariance
structures in these two spaces. A PLS model will try to find the
multidimensional direction in the X space that explains the maximum
multidimensional variance direction in the Y space. PLS-regression is
particularly suited when the matrix of predictors has more variables
than observations, and when there is multicollinearity among X
values. By contrast, standard regression will fail in these cases.
Classes included in this module are :class:`PLSRegression`
:class:`PLSCanonical`, :class:`CCA` and :class:`PLSSVD`
.. topic:: Reference:
* JA Wegelin
`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>`_
.. topic:: Examples:
* :ref:`example_plot_pls.py`
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