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3.6. Partial Least Squares

Partial least squares (PLS) models are useful to find linear relations between two multivariate datasets: in PLS the X and Y arguments of the fit method are 2D arrays.


PLS finds the fundamental relations between two matrices (X and Y): it is a latent variable approach to modeling the covariance structures in these two spaces. A PLS model will try to find the multidimensional direction in the X space that explains the maximum multidimensional variance direction in the Y space. PLS-regression is particularly suited when the matrix of predictors has more variables than observations, and when there is multicollinearity among X values. By contrast, standard regression will fail in these cases.

Classes included in this module are PLSRegression PLSCanonical, CCA and PLSSVD