Linear Regression


A linear regression algorithm with optional L1 (LASSO), L2 (ridge) or L1L2 (elastic net) regularization.



  • Data

    A data set

  • Preprocessor

    A preprocessed data set.


  • Learner

    A linear regression learning algorithm with settings as specified in the dialog.

  • Predictor

    A trained regressor. Output signal sent only if input Data is present.


The Linear Regression widget constructs a learner/predictor that learns a linear function from its input data. The model can identify the relationship between a predictor xi and the response variable y. Additionally, Lasso and Ridge regularization parameters can be specified. Lasso regression minimizes a penalized version of the least squares loss function with L1-norm penalty and Ridge regularization with L2-norm penalty.

Linear regreesion works only on regression tasks.

  1. The learner/predictor name
  2. Choose a model to train:
    • no regularization
    • a Ridge regularization (L2-norm penalty)
    • a Lasso bound (L1-norm penalty)
    • an Elastic net regularization
  3. Produce a report.
  4. Press Apply to commit changes. If Apply Automatically is ticked, changes are committed automatically.


Below, is a simple workflow with housing data set. We trained Linear Regression and Random Forest and evaluated their performance in Test&Score.