.. _`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



**Documentation**

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.




*Input ports*:


*Output ports*:
    **model** : model
        Model


**Definition**


*Input ports*


*Output ports*

    :model:  model

        Model





.. automodule:: node_decomposition

.. class:: PLSRegressionCrossDecomposition