8.21.4. 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
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.