LARS is an extension of the LASSO, which constrains regression coefficients to no more than a possible absolute sum. The LARS algorithm can be understood as recalculating the regression model step by step while slowly relaxing the LASSO constraint. The result of this is analogous to a forward stepwise selection algorithm, but is valid, whereas forward stepwise selection is not.
Stats
created |
4 months ago |
viewed |
3 times |
active |
4 months ago |
editors |
1 |
Recent Hot Answers
What problem do shrinkage methods solve?Advantages of doing “double lasso” or performing lasso twice?
What problem do shrinkage methods solve?
Feature selection with k-fold cross-validated least angle regression
What problem do shrinkage methods solve?
more »
Related Tags
lasso × 5feature-selection × 3
r × 2
matlab × 2
ridge-regression × 2
glmnet
classification
regression
correlation
proof
generalized-least-squares
time-series
forecasting
shrinkage
regularization
