Why is LASSO used for feature selection?

Why is LASSO used for 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.

Why would you want to use LASSO instead of ridge regression?

In the ridge, the coefficients of the linear transformation are normal distributed and in the lasso they are Laplace distributed. In the lasso, this makes it easier for the coefficients to be zero and therefore easier to eliminate some of your input variable as not contributing to the output.

What are two problems with stepwise regression?

The principal drawbacks of stepwise multiple regression include bias in parameter estimation, inconsistencies among model selection algorithms, an inherent (but often overlooked) problem of multiple hypothesis testing, and an inappropriate focus or reliance on a single best model.

How does Lasso regression perform model selection?

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.

Is lasso used for variable 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.

Is lasso or ridge better for feature selection?

Ridge regression performs better when the data consists of features which are sure to be more relevant and useful. mathematically, Lasso is = Residual Sum of Squares + λ * (Sum of the absolute value of the magnitude of coefficients).

What is one advantage of using LASSO over ridge regression for a linear regression problem?

One obvious advantage of lasso regression over ridge regression, is that it produces simpler and more interpretable models that incorporate only a reduced set of the predictors.

When would you prefer using lasso regression instead of ridge regression?

Lasso tends to do well if there are a small number of significant parameters and the others are close to zero (ergo: when only a few predictors actually influence the response). Ridge works well if there are many large parameters of about the same value (ergo: when most predictors impact the response).

What is the problem with stepwise regression?

A fundamental problem with stepwise regression is that some real explanatory variables that have causal effects on the dependent variable may happen to not be statistically significant, while nuisance variables may be coincidentally significant.

Is lasso better than stepwise?

LASSO is much faster than forward stepwise regression. There is obviously a great deal of overlap between feature selection and prediction, but I never tell you about how well a wrench serves as a hammer.

Why does lasso regression work?

Lasso regression is like linear regression, but it uses a technique “shrinkage” where the coefficients of determination are shrunk towards zero. The lasso regression allows you to shrink or regularize these coefficients to avoid overfitting and make them work better on different datasets.

Does lasso regression have unique solution?

The lasso solution is unique when rank(X) = p, because the criterion is strictly convex. But the criterion is not strictly convex when rank(X) < p, and so there can be multiple minimizers of the lasso criterion (emphasized by the element notation in (1)).

Is lasso better than stepwise variable selection?

Although LASSO is not perfect, it’s a lot better – that is, when run on artificial problems where we know the right answer, it does a lot better than stepwise. Really, though, all automated methods of variable selection are deeply flawed; LASSO should only be used when you don’t understand the substance of your model very well.

Is multicollinearity an issue when doing stepwise logistic regression using AIC and Bic?

Is multicollinearity an issue when doing stepwise logistic regression using AIC and BIC? As far as I understood, it should not be a problem as long as I don’t have perfect multicollinearity since I don’t mind if the standard errors get inflated. However, what about using the Likelihood-ratio test to do feature selection?

What are the strengths and limitations of the LASSO model selection?

The LASSO and forward/backward model selection both have strengths and limitations. No far sweeping recommendation can be made. Simulation can always be explored to address this. Both can be understood in the sense of dimensionality: referring to p the number of model parameters and n the number of observations.

What is lasso and how does it work?

LASSO is an attempt to remedy these problems by penalizing the model for complexity and adjusting parameters towards 0. Although LASSO is not perfect, it’s a lot better – that is, when run on artificial problems where we know the right answer, it does a lot better than stepwise.