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

Attributes

coef_

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

n_iter_

Number of iterations of the power method, for each component.

x_loadings_

The loadings of X.

x_rotations_

The projection matrix used to transform X.

x_scores_

The transformed training samples.

x_weights_

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

y_loadings_

The loadings of Y.

y_rotations_

The projection matrix used to transform Y.

y_scores_

The transformed training targets.

y_weights_

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

Definition

Output ports

model model

Model

Configuration

Max iterations (max_iter)

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

Number of components to keep (n_components)

Number of components to keep. Should be in [1, min(n_samples, n_features, n_targets)].

Scale the data (scale)

Whether to scale X and Y.

Tolerance (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.

Implementation

class node_decomposition.PLSRegressionCrossDecomposition