Linear Regression

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


  • Data: input dataset

  • Preprocessor: preprocessing method(s)


  • Learner: linear regression learning algorithm

  • Model: trained model

  • Coefficients: linear regression coefficients

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 regression 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 dataset. We trained Linear Regression and [Random Forest](../model/ and evaluated their performance in [Test & Score](../evaluate/