Statements (23)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:regression_method
gptkb:statistical_analysis |
| gptkbp:application |
feature selection
predictive modeling |
| gptkbp:category |
supervised learning
|
| gptkbp:combines |
L1 regularization
L2 regularization |
| gptkbp:hyperparameter |
alpha
l1_ratio |
| gptkbp:implementedIn |
gptkb:scikit-learn
R |
| gptkbp:introduced |
gptkb:Zou_and_Hastie
|
| gptkbp:introducedIn |
2005
|
| gptkbp:objective |
minimizes least squares with L1 and L2 penalties
|
| gptkbp:relatedTo |
gptkb:Ridge_regression
gptkb:Lasso_regression |
| gptkbp:solvedBy |
variable selection
multicollinearity |
| gptkbp:usedIn |
gptkb:machine_learning
statistics |
| gptkbp:bfsParent |
gptkb:Regression_Models
|
| gptkbp:bfsLayer |
7
|
| https://www.w3.org/2000/01/rdf-schema#label |
Elastic Net Regression
|