Difference between revisions of "Orange: Linear Regression"
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Revision as of 20:49, 12 January 2020
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.
The learner/predictor name
Choose a model to train:
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.
Below, is a simple workflow with housing dataset. We trained Linear Regression and Random Forest and evaluated their performance in Test & Score.