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