Partial Least Squares cross-decomposition (PLS regression)

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

n_components (n_components)

Number of components to keep.

scale (scale)

whether to scale the data

max_iter (max_iter)

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

tol (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