PLS¶
Partial Least Squares Regression widget for multivariate data analysis.
Inputs
- Data: input dataset
- Preprocessor: preprocessing method(s)
Outputs
- 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.
- The learner/predictor name
- 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
- Press Apply to commit changes. If Apply Automatically is ticked, changes are committed automatically.
Example¶
Below, is a simple workflow with housing dataset. We trained PLS and Linear Regression and evaluated their performance in Test & Score.