# `sklearn.cross_decomposition`.PLSSVD¶

class `sklearn.cross_decomposition.``PLSSVD`(n_components=2, scale=True, copy=True)[source]

Partial Least Square SVD

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

Read more in the User Guide.

Parameters: n_components : int, default 2 Number of components to keep. scale : boolean, default True Whether to scale X and Y. copy : boolean, default True Whether to copy X and Y, or perform in-place computations. 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) `fit_transform`(X[, y]) Learn and apply the dimension reduction on the train data. `get_params`([deep]) Get parameters for this estimator. `set_params`(\*\*params) Set the parameters of this estimator. `transform`(X[, Y]) Apply the dimension reduction learned on the train data.
`__init__`(n_components=2, scale=True, copy=True)[source]
`fit_transform`(X, y=None, **fit_params)[source]

Learn and apply the dimension reduction on the train data.

Parameters: X : array-like of predictors, shape = [n_samples, p] Training vectors, where n_samples in the number of samples and p is the number of predictors. Y : array-like of response, shape = [n_samples, q], optional Training vectors, where n_samples in the number of samples and q is the number of response variables. x_scores if Y is not given, (x_scores, y_scores) otherwise. :
`get_params`(deep=True)[source]

Get parameters for this estimator.

Parameters: deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. params : mapping of string to any Parameter names mapped to their values.
`set_params`(**params)[source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter 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)[source]

Apply the dimension reduction learned on the train data.