# 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.

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.

## Example

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