Contents
Why use Least Angle Regression?
The advantages of the LARS method are: It is computationally just as fast as forward selection. It produces a full piecewise linear solution path, which is useful in cross-validation or similar attempts to tune the model.
What is lasso LARS?
Lasso model fit with Least Angle Regression a.k.a. Lars. It is a Linear Model trained with an L1 prior as regularizer.
What is regularization path in Lasso?
The regularization path of the Lasso can be shown to be piecewise linear, making it pos- sible to “follow” and explicitly compute the entire path. In particu- lar, the ℓ1-norm has been used for that purpose in the Lasso formulation (Tibshirani, 1996).
When to use least angle regression in regression?
Least Angle Regression (aka LARS) is a model selection method for linear regression (when you’re worried about overfitting or want your model to be easily interpretable). To motivate it, let’s consider some other model selection methods:
How to find the least angle in a model?
Start with no variables in your model. Find the variable most correlated with the residual. (Note that the variable most correlated with the residual is equivalently the one that makes the least angle with the residual, whence the name.) Move in the direction of this variable until some other variable is just as correlated.
When to use the LARS method of regression?
It is easily modified to produce efficient algorithms for other methods producing similar results, like the lasso and forward stagewise regression. It is effective in contexts where p >> n (i.e., when the number of dimensions is significantly greater than the number of points). The disadvantages of the LARS method include:
Which is more stable lasso or forward Stagewise regression?
The algorithm thus behaves as intuition would expect, and also is more stable. It is easily modified to produce efficient algorithms for other methods producing similar results, like the lasso and forward stagewise regression.