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

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. Should be in [1, min(n_samples, n_features, n_targets)].

• scale

  Whether to scale X and Y.

• max_iter

  The maximum number of iterations of the power method when algorithm=’nipals’. Ignored otherwise.

• tol

  The tolerance used as convergence criteria in the power method: the algorithm stops whenever the squared norm of u_i - u_{i-1} is less than tol, where u corresponds to the left singular vector.

Attributes:

• x_weights_

  The left singular vectors of the cross-covariance matrices of each iteration.

• y_weights_

  The right singular vectors of the cross-covariance matrices of each iteration.

• x_loadings_

  The loadings of X.

• y_loadings_

  The loadings of Y.

• x_scores_

  The transformed training samples.

• y_scores_

  The transformed training targets.

• x_rotations_

  The projection matrix used to transform X.

• y_rotations_

  The projection matrix used to transform Y.

• coef_

  The coefficients of the linear model such that Y is approximated as Y = X @ coef_.

• n_iter_

  Number of iterations of the power method, for each component. Empty if algorithm=’svd’.

Input ports:

Output ports:

modelmodel

Model

Definition

Input ports

Output ports

model

model

Model

class node_decomposition.PLSRegressionCrossDecomposition