Difference between revisions of "Orange: Linear Regression"

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Revision as of 20:49, 12 January 2020

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.

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.

../../_images/LinearRegression-stamped.png

   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.

Example

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

../../_images/LinearRegression-regression.png


Referensi

Pranala Menarik