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](https://en.wikipedia.org/wiki/Linear_regression) from its input data. The model can identify the relationship between a predictor xi and the response variable y. Additionally, [Lasso](https://en.wikipedia.org/wiki/Least_squares#Lasso_method) and [Ridge](https://en.wikipedia.org/wiki/Least_squares#Lasso_method) 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.

![](images/LinearRegression-stamped.png)

  1. The learner/predictor name

  2. Choose a model to train: - no regularization - a [Ridge](https://en.wikipedia.org/wiki/Least_squares#Lasso_method) regularization (L2-norm penalty) - a [Lasso](https://en.wikipedia.org/wiki/Least_squares#Lasso_method) bound (L1-norm penalty) - an [Elastic net](https://en.wikipedia.org/wiki/Elastic_net_regularization) regularization

  3. Produce a report.

  4. 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](../model/randomforest.md) and evaluated their performance in [Test & Score](../evaluate/testandscore.md).

![](images/LinearRegression-regression.png)