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 inplace computations.
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.
See also
Methods
fit
(X, Y)Fit model to data. 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. 
fit
(X, Y)[source]¶ Fit model to data.
Parameters: X : arraylike, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples and n_features is the number of predictors.
Y : arraylike, shape = [n_samples, n_targets]
Target vectors, where n_samples is the number of samples and n_targets is the number of response variables.

fit_transform
(X, y=None)[source]¶ Learn and apply the dimension reduction on the train data.
Parameters: X : arraylike, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples and n_features is the number of predictors.
y : arraylike, shape = [n_samples, n_targets]
Target vectors, where n_samples is the number of samples and n_targets is the number of response variables.
Returns: 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.
Returns: 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.
Parameters: X : arraylike, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples and n_features is the number of predictors.
Y : arraylike, shape = [n_samples, n_targets]
Target vectors, where n_samples is the number of samples and n_targets is the number of response variables.
