.. _`Partial Least Squares cross-decomposition (PLS regression)`:

.. _`org.sysess.sympathy.machinelearning.pls`:

Partial Least Squares cross-decomposition (PLS regression)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. image:: PCA.svg
   :width: 48


Finds the fundamental relations between two matrices X and Y, ie. it finds the (multidimensional) direction in X that best explains maximum multidimensional direction in Y. See also PCA-analysis

:Configuration:


  - *n_components*

    Number of components to keep.


  - *scale*

    whether to scale the data


  - *max_iter*

    the maximum number of iterations of the NIPALS inner loop (used
    only if algorithm="nipals")


  - *tol*

    Tolerance used in the iterative algorithm default 1e-06.




:Attributes:


  - *x_weights_*

    X block weights vectors.


  - *y_weights_*

    Y block weights vectors.


  - *x_loadings_*

    X block loadings vectors.


  - *y_loadings_*

    Y block loadings vectors.


  - *x_scores_*

    X scores.


  - *y_scores_*

    Y scores.


  - *x_rotations_*

    X block to latents rotations.


  - *y_rotations_*

    Y block to latents rotations.


  - *coef_*

    The coefficients of the linear model: ``Y = X coef_ + Err``


  - *n_iter_*

    Number of iterations of the NIPALS inner loop for each
    component.




:Inputs:


:Outputs:
    **model** : model
        Model

*Ports*:

    **Outputs**:

        :model: model

            Model

*Configuration*:

    **n_components**
        Number of components to keep.
    **scale**
        whether to scale the data
    **max_iter**
        the maximum number of iterations of the NIPALS inner loop (used
        only if algorithm="nipals")
    **tol**
        Tolerance used in the iterative algorithm default 1e-06.

.. automodule:: node_decomposition

.. class:: PLSRegressionCrossDecomposition