What are the advantages of Lasso regression?

What are the advantages of Lasso regression?

Lasso regression helps in feature selection and reduces overfitting by penalizing large coefficients.

Can you explain how it performs feature selection?

Sure! Lasso regression adds a penalty equal to the absolute value of the magnitude of coefficients, which can shrink some coefficients to zero, effectively selecting a simpler model.

What situations is Lasso regression particularly useful in?

It's particularly useful when you have a large number of features, especially when some of them are irrelevant or redundant.

Are there any limitations to using Lasso regression?

Yes, Lasso can struggle with highly correlated features, as it tends to arbitrarily select one and ignore others.

How does Lasso compare to Ridge regression?

Lasso can reduce coefficients to zero, while Ridge regression shrinks them but keeps all features in the model.