# Linear Regression¶

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

## Signals¶

**Inputs**:

**Data**A data set

**Preprocessor**A preprocessed data set.

**Outputs**:

**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.

## Description¶

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* data set. We trained **Linear Regression** and Random Forest and evaluated their performance in Test&Score.