Contents
Can you use LASSO for classification?
1 Answer. You can use the Lasso or elastic net regularization for generalized linear model regression which can be used for classification problems. Here data is the data matrix with rows as observations and columns as features. group is the labels.
What is LASSO feature selection?
In statistics and machine learning, lasso (least absolute shrinkage and selection operator; also Lasso or LASSO) is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the resulting statistical model.
What is group LASSO?
The group lasso is an extension of the lasso to do variable selection on (predefined) groups of variables in linear regression models. We further use a two-stage procedure which aims for sparser models than the group lasso, leading to improved prediction performance for some cases.
How to use Lasso regression for feature selection?
So, the idea of using Lasso regression for feature selection purposes is very simple: we fit a Lasso regression on a scaled version of our dataset and we consider only those features that have a coefficient different from 0. Obviously, we first need to tune α hyperparameter in order to have the right kind of Lasso regression.
What is the consistency of the LASSO model?
On the model selection consistency front, Meinshausen and Buhlmann (2006) have shown that under a set of conditions, Lasso is consistent in estimating the dependency between Gaussian variables even when the number of variables p grow faster than n. Leng et al.
What does Lasso mean for categorical variables in regression?
That means that if one of these dummies is included, your model now has a parameter whose interpretation is “the difference between one level of this factor and an arbitrarily chosen other level of that factor”. And maybe none of the other dummies for that factor were selected.
Can a lasso be used with factor variables?
I know that having factor variables doesn’t really work in LASSO through either lars or glmnet, but the variables are too many and there are too many different, unordered values they can take on to reasonably recode them numerically. Can LASSO be used in this situation? How do I do this?