Partial Least Squares cross-decomposition (PLS regression)

../../../../_images/PCA.svg

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.

Some of the docstrings for this module have been automatically extracted from the scikit-learn library and are covered by their respective licenses.

class node_decomposition.PLSRegressionCrossDecomposition[source]