# Linear Regression¶

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

- Inputs
- Data
- input dataset
- Preprocessor
- preprocessing method(s)

- Outputs
- 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 regreesion works only on regression tasks.

- The learner/predictor name
- Choose a model to train:
- no regularization
- a Ridge regularization (L2-norm penalty)
- a Lasso bound (L1-norm penalty)
- an Elastic net regularization

- Produce a report.
- 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 **Linear Regression** and Random Forest and evaluated their performance in Test&Score.