Learns a linear function of its input data.
A data set
A preprocessed data set.
A learning algorithm with the supplied parameters
A trained regressor. Signal Predictor sends the output signal only if signal Data is present.
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.
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.