Partial least squares (PLS) regression is a technique that reduces the predictors to a smaller set of uncorrelated components and performs least squares regression on these components, instead of on the original data.

What is partial least squares regression used for?

The Partial Least Squares regression (PLS) is a method which reduces the variables, used to predict, to a smaller set of predictors. These predictors are then used to perfom a regression. Some programs differentiate PLS 1 from PLS 2. PLS 1 corresponds to the case where there is only one dependent variable.

What is the difference between OLS and PLS?

In the PLS regression two components yields, R² and predicted R² were 70% and 49.4% respectively . CONCLUSION : these findings indicated that the PLS model provides much more stable results than the OLS model when sample size is small and there are data missing values and multicollinearity .