Orange: Linear Regression

Sumber: https://docs.biolab.si//3/visual-programming/widgets/model/linearregression.html

A linear regression algorithm with optional L1 (LASSO), L2 (ridge) or L1L2 (elastic net) regularization.

Input

Data: input dataset
Preprocessor: preprocessing method(s)

Output

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:
  • no regularization
  • 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.

Contoh

Below, is a simple workflow with housing dataset. We trained Linear Regression and Random Forest and evaluated their performance in Test & Score.

Referensi

• https://docs.biolab.si//3/visual-programming/widgets/model/linearregression.html

Pranala Menarik

• Orange