Statements (33)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
statistical analysis
regression analysis method |
| gptkbp:alsoKnownAs |
gptkb:Least_Absolute_Shrinkage_and_Selection_Operator
|
| gptkbp:application |
gptkb:signal_processing
finance genomics image analysis |
| gptkbp:feature |
can shrink some coefficients to zero
performs feature selection |
| gptkbp:form |
minimize (1/2n)||y - Xβ||^2_2 + λ||β||_1
|
| https://www.w3.org/2000/01/rdf-schema#label |
lasso regression
|
| gptkbp:hyperparameter |
lambda (regularization parameter)
|
| gptkbp:implementedIn |
gptkb:MATLAB
gptkb:scikit-learn R |
| gptkbp:introduced |
gptkb:Robert_Tibshirani
|
| gptkbp:introducedIn |
1996
|
| gptkbp:limitation |
can select at most n variables if n < p
can be unstable with highly correlated predictors |
| gptkbp:objective |
minimize sum of squared errors plus lambda times sum of absolute values of coefficients
|
| gptkbp:penalty |
L1 norm
|
| gptkbp:relatedTo |
gptkb:elastic_net
ridge regression |
| gptkbp:solvedBy |
gptkb:least_angle_regression_(LARS)
coordinate descent subgradient methods |
| gptkbp:usedFor |
regularization
variable selection preventing overfitting |
| gptkbp:usedIn |
gptkb:machine_learning
statistics |
| gptkbp:bfsParent |
gptkb:Regression_analysis
|
| gptkbp:bfsLayer |
6
|