Partial Least Squares Regression widget for multivariate data analysis.


  • Data: input dataset

  • Preprocessor: preprocessing method(s)


  • Learner: PLS regression learning algorithm

  • Model: trained model

  • Coefficients: PLS regression coefficients

PLS (Partial Least Squares) widget acts as a regressor for data with numeric target variable. In its current implementation, it is the same as linear regression, but with a different kind of regularization. Here, regularization is performed with the choice of the components - the more components, the lesser the effect of regularization.

PLS widget can output coefficients, just like Linear Regression. One can observe the effect of each variable in a Data Table.


  1. The learner/predictor name

  2. Parameters:

    • Components: the number of components of the model, which act as regularization (the more components, the lesser the regularization)

    • Iteration limit: maximum iterations for stopping the algorithm

  3. Press Apply to commit changes. If Apply Automatically is ticked, changes are committed automatically.


Below, is a simple workflow with housing dataset. We trained PLS and Linear Regression and evaluated their performance in Test & Score.