Regressors with variable selection

ElasticNet/ElasticNetCV/Lars/LarsCV/Lasso/LassoCV/LassoLars/LassoLarsCV/LassoLarsIC/OrthogonalMatchingPursuit/OrthogonalMatchingPursuitCV

Common

Input Data

Inputs

*Target Data

Data Input

*Model Name

Name of the user-defined linear model

Predict Output

If not empty, the model will additionally make prediction on Target Data and store

the result here.

Merge Data

Predict Output will be created with predicted labels concatenated to Merge Data.

*x (numeric)

User-selected columns to be x.

  • One or more columns should be selected.

*y (numeric)

User-selected columns to be y.

  • Only one column should be selected.

Workflow Example

Python Package

ElasticNet: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html

ElasticNetCV: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNetCV.html

Lars: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lars.html

LarsCV: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LarsCV.html

Lasso: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html

LassoCV: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoCV.html

LassoLars: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoLars.html

LassoLarsCV: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoLarsCV.html

LassoLarsIC: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoLarsIC.html

OrthogonalMatchingPursuit: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.OrthogonalMatchingPursuit.html

OrthogonalMatchingPursuitCV: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.OrthogonalMatchingPursuitCV.html

PreviousClassical linear regressorsNextBayesian Reg.

Last updated