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Is Lasso good for feature selection?
Lasso regression has a very powerful built-in feature selection capability that can be used in several situations. For example, if the relationship between the features and the target variable is not linear, using a linear model might not be a good idea.
How does lasso choose variables?
Lasso does regression analysis using a shrinkage parameter “where data are shrunk to a certain central point” [1] and performs variable selection by forcing the coefficients of “not-so-significant” variables to become zero through a penalty. …
How to fit a lasso logistic model in R?
There is a package in R called glmnet that can fit a LASSO logistic model for you! This will be more straightforward than the approach you are considering. More precisely, glmnet is a hybrid between LASSO and Ridge regression but you may set a parameter α = 1 to do a pure LASSO model.
Can a lasso’s norm penalty be used in logistic regression?
2 Answers. You may be able to validate the procedure on a particular data-set, but it doesn’t seem safe in general, or to offer any advantage over a stepwise logistic regression. And of course it’s unnecessary; LASSO’s -norm penalty can be used for shrinkage & selection in logistic regression.
How does Lasso work in a linear model?
In linear model regularisation, the penalty is applied over the coefficients that multiply each of the predictors. From the different types of regularisation, Lasso or L1 has the property that is able to shrink some of the coefficients to zero. Therefore, that feature can be removed from the model.
How to select features using lasso regularisation problem?
From the different types of regularisation, Lasso or L1 has the property that is able to shrink some of the coefficients to zero. Therefore, that feature can be removed from the model. In this post I will de m onstrate how to select features using the Lasso regularisation classification problem.