|
gptkbp:instanceOf
|
Regression technique
|
|
gptkbp:alsoKnownAs
|
gptkb:Least_Absolute_Shrinkage_and_Selection_Operator
|
|
gptkbp:assumes
|
Linear relationship between variables
|
|
gptkbp:canSetCoefficientsToZero
|
Yes
|
|
gptkbp:category
|
gptkb:Supervised_learning
Linear model
|
|
gptkbp:contrastsWith
|
gptkb:Elastic_Net
gptkb:Ridge_Regression
|
|
gptkbp:effectOnCoefficients
|
Sparsity
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Lasso Regression
|
|
gptkbp:hyperparameter
|
Regularization strength (lambda)
|
|
gptkbp:implementedIn
|
gptkb:MATLAB
gptkb:scikit-learn
R
|
|
gptkbp:introduced
|
gptkb:Robert_Tibshirani
|
|
gptkbp:introducedIn
|
1996
|
|
gptkbp:limitation
|
Can be unstable with correlated predictors
Can select at most n variables if n < p
|
|
gptkbp:objective
|
Least squares with L1 regularization
|
|
gptkbp:penalty
|
L1 penalty
|
|
gptkbp:reduces
|
Sum of squared errors plus L1 norm of coefficients
|
|
gptkbp:relatedTo
|
Subset selection
Shrinkage methods
|
|
gptkbp:shrinksCoefficients
|
Yes
|
|
gptkbp:solvedBy
|
Coordinate descent
Least angle regression
|
|
gptkbp:usedFor
|
Regularization
Feature selection
|
|
gptkbp:usedIn
|
gptkb:Machine_Learning
Statistics
|
|
gptkbp:bfsParent
|
gptkb:Linear_Model
|
|
gptkbp:bfsLayer
|
5
|