class sklearn.cross_decomposition.PLSRegression(n_components=2, scale=True, max_iter=500, tol=1e06, copy=True)
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PLS regression
PLSRegression implements the PLS 2 blocks regression known as PLS2 or PLS1 in case of one dimensional response. This class inherits from _PLS with mode=”A”, deflation_mode=”regression”, norm_y_weights=False and algorithm=”nipals”.
Read more in the User Guide.
Parameters: 


Attributes: 

Matrices:
T: x_scores_ U: y_scores_ W: x_weights_ C: y_weights_ P: x_loadings_ Q: y_loadings__
Are computed such that:
X = T P.T + Err and Y = U Q.T + Err T[:, k] = Xk W[:, k] for k in range(n_components) U[:, k] = Yk C[:, k] for k in range(n_components) x_rotations_ = W (P.T W)^(1) y_rotations_ = C (Q.T C)^(1)
where Xk and Yk are residual matrices at iteration k.
For each component k, find weights u, v that optimizes: max corr(Xk u, Yk v) * std(Xk u) std(Yk u)
, such that u = 1
Note that it maximizes both the correlations between the scores and the intrablock variances.
The residual matrix of X (Xk+1) block is obtained by the deflation on the current X score: x_score.
The residual matrix of Y (Yk+1) block is obtained by deflation on the current X score. This performs the PLS regression known as PLS2. This mode is prediction oriented.
This implementation provides the same results that 3 PLS packages provided in the R language (Rproject):
Jacob A. Wegelin. A survey of Partial Least Squares (PLS) methods, with emphasis on the twoblock case. Technical Report 371, Department of Statistics, University of Washington, Seattle, 2000.
In french but still a reference: Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris: Editions Technic.
>>> from sklearn.cross_decomposition import PLSRegression >>> X = [[0., 0., 1.], [1.,0.,0.], [2.,2.,2.], [2.,5.,4.]] >>> Y = [[0.1, 0.2], [0.9, 1.1], [6.2, 5.9], [11.9, 12.3]] >>> pls2 = PLSRegression(n_components=2) >>> pls2.fit(X, Y) ... PLSRegression(copy=True, max_iter=500, n_components=2, scale=True, tol=1e06) >>> Y_pred = pls2.predict(X)
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. 
predict (X[, copy])  Apply the dimension reduction learned on the train data. 
score (X, y[, sample_weight])  Returns the coefficient of determination R^2 of the prediction. 
set_params (**params)  Set the parameters of this estimator. 
transform (X[, Y, copy])  Apply the dimension reduction learned on the train data. 
__init__(n_components=2, scale=True, max_iter=500, tol=1e06, copy=True)
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fit(X, Y)
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Fit model to data.
Parameters: 


fit_transform(X, y=None)
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Learn and apply the dimension reduction on the train data.
Parameters: 


Returns: 

get_params(deep=True)
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Get parameters for this estimator.
Parameters: 


Returns: 

predict(X, copy=True)
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Apply the dimension reduction learned on the train data.
Parameters: 


This call requires the estimation of a p x q matrix, which may be an issue in high dimensional space.
score(X, y, sample_weight=None)
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Returns the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1  u/v), where u is the residual sum of squares ((y_true  y_pred) ** 2).sum() and v is the total sum of squares ((y_true  y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.
Parameters: 


Returns: 

set_params(**params)
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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: 


transform(X, Y=None, copy=True)
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Apply the dimension reduction learned on the train data.
Parameters: 


Returns: 

sklearn.cross_decomposition.PLSRegression
© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.cross_decomposition.PLSRegression.html