Which is better for feature selection lasso or ridge?

Lasso produces sparse solutions and as such is very useful selecting a strong subset of features for improving model performance. Ridge regression on the other hand can be used for data interpretation due to its stability and the fact that useful features tend to have non-zero coefficients.

Is elastic net always better?

Yes, elastic net is always preferred over lasso & ridge regression because it solves the limitations of both methods, while also including each as special cases. So if the ridge or lasso solution is, indeed, the best, then any good model selection routine will identify that as part of the modeling process.

Why is Lasso better for feature selection?

LASSO involves a penalty factor that determines how many features are retained; using cross-validation to choose the penalty factor helps assure that the model will generalize well to future data samples.

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.

Why is ridge regression better?

Ridge regression is a better predictor than least squares regression when the predictor variables are more than the observations. Ridge regression works with the advantage of not requiring unbiased estimators – rather, it adds bias to estimators to reduce the standard error.

Is lasso better than least squares?

(a) The lasso, relative to least squares, is: More flexible and hence will give improved prediction accuracy when it increase in bias is less than its decrease in variance. More flexible and hence will give improved prediction accuracy when its increase in variance is less than its decrease in bias.

Why is ridge regression bad?

Large variance will increase the mean square error, thus making the estimator bad. Ridge regression will give a bias estimator. The bias will depend on the ridge constant k so that it is required to choose the optimal Ridge constant k to minimize the bias.

What is the benefit of lasso regression?

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. However, neither ridge regression nor the lasso will universally dominate the other.

What is the difference between lasso and Ridge regression?

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.