A linear regression algorithm with optional L1 (LASSO), L2 (ridge) or L1L2 (elastic net) regularization.
Data: input dataset
Preprocessor: preprocessing method(s)
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 regression works only on regression tasks.