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 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 regreesion works only on regression tasks.

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  1. The learner/predictor name
  2. Choose a model to train:
    • no regularization
    • a Ridge regularization (L2-norm penalty)
    • a Lasso bound (L1-norm penalty)
    • an Elastic net 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 and evaluated their performance in Test&Score.

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