# Linear Regression¶

Learns a linear function of its input data.

## Signals¶

**Inputs**:

**Data**A data set

**Preprocessor**A preprocessed data set.

**Outputs**:

**Learner**A learning algorithm with the supplied parameters

**Predictor**A trained regressor. Signal

*Predictor*sends the output signal only if signal*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.

- 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 showing how to use both the *Predictor* and
the *Learner* output. We used the *Housing* data set. For the *Predictor*, we input the prediction model
into the Predictions widget and view the results in the Data Table. For the
*Learner*, we can compare different learners in the Test&Score widget.