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 regression 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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