Can LASSO be used for variable selection Why or why not What about ridge regression?

The LASSO, on the other hand, handles estimation in the many predictors framework and performs variable selection. Both ridge regression and the LASSO can outperform OLS regression in some predictive situations – exploiting the tradeoff between variance and bias in the mean square error.

Why does the LASSO provide variable selection?

“The lasso performs L1 shrinkage, so that there are “corners” in the constraint, which in two dimensions corresponds to a diamond. If the sum of squares “hits” one of these corners, then the coefficient corresponding to the axis is shrunk to zero.

Can Lasso regression be used 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 regression do feature selection?

How can we use it for feature selection? Trying to minimize the cost function, Lasso regression will automatically select those features that are useful, discarding the useless or redundant features. In Lasso regression, discarding a feature will make its coefficient equal to 0.

How random forest LASSO and ridge regression differ between LASSO and Ridge?

Lasso is a modification of linear regression, where the model is penalized for the sum of absolute values of the weights. Ridge takes a step further and penalizes the model for the sum of squared value of the weights.

How does lasso regression select 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 does LASSO perform feature selection?

The LASSO method regularizes model parameters by shrinking the regression coefficients, reducing some of them to zero. The feature selection phase occurs after the shrinkage, where every non-zero value is selected to be used in the model. The larger λ becomes, then the more coefficients are forced to be zero.