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Is lasso regression or classification?
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 lambda in LASSO?
The tuning parameter lambda is chosen by cross validation. When lambda is small, the result is essentially the least squares estimates. As lambda increases, shrinkage occurs so that variables that are at zero can be thrown away. Let’s build lasso and ridge regression models on continous dependent variable.
Why does lasso regression work?
Lasso regression penalizes less important features of your dataset and makes their respective coefficients zero, thereby eliminating them. Thus it provides you with the benefit of feature selection and simple model creation. So, if the dataset has high dimensionality and high correlation, lasso regression can be used.
Which is an extension of the LASSO method?
The group lasso is an extension of the lasso to do variable selection on (predefined) groups of variables in linear regression models. The estimates have the attractive property of being invariant under groupwise orthogonal reparameterizations.
Why is the group lasso solution path not piecewise linear?
Why is the group lasso solution path not piecewise linear? Intuitively speaking, the group lasso can be preferred to the lasso since it provides a means for us to incorporate (a certain type of) additional information into our estimate for the true coefficient β ∗. As an extreme scenario, considering the following:
Can you use group lasso in glmnet?
I am using glmnet for LASSO. My data set contains several continuous variables and one categorical variable (it has four levels). I wondered if I could treat three dummy variables as other continuous variables. Should I use a type of group LASSO approach for the three dummies? As far as I am aware glmnet doesn’t have this feature implemented yet.