9.12.4. sklearn.pls.PLSSVD

class sklearn.pls.PLSSVD(n_components=2, scale=True, copy=True)

Partial Least Square SVD

Simply perform a svd on the crosscovariance matrix: X’Y The are no iterative deflation here.

Parameters :

X: array-like of predictors, shape = [n_samples, p] :

Training vector, where n_samples in the number of samples and p is the number of predictors. X will be centered before any analysis.

Y: array-like of response, shape = [n_samples, q] :

Training vector, where n_samples in the number of samples and q is the number of response variables. X will be centered before any analysis.

n_components: int, (default 2). :

number of components to keep.

scale: boolean, (default True) :

scale X and Y

See also

PLSCanonical, CCA

Attributes

x_weights_: array, [p, n_components]	X block weights vectors.
y_weights_: array, [q, n_components]	Y block weights vectors.
x_scores_: array, [n_samples, n_components]	X scores.
y_scores_: array, [n_samples, n_components]	Y scores.

Methods

fit(X, Y)
set_params(**params)	Set the parameters of the estimator.
transform(X[, Y])	Apply the dimension reduction learned on the train data.

__init__(n_components=2, scale=True, copy=True)

set_params(**params)

Set the parameters of the estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns :self :

transform(X, Y=None)

Apply the dimension reduction learned on the train data.