PLS partial least squares regression maximizes the variance of the predictors = X and maximizes the correlation between X and the variable to be explained Y.
This algorithm borrows its approach from both principal component analysis and regression.
More precisely, PLS regression looks for components, called latent variables, related to X and Y, serving to express the regression of Y on these variables and finally of Y on X.
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