What is feature selection in Lasso regression?

What is feature selection in Lasso regression?

Feature selection in Lasso regression involves selecting a subset of relevant features by applying L1 regularization.

How does Lasso regression differ from Ridge regression in feature selection?

Lasso regression can shrink some coefficients to zero, effectively selecting features, while Ridge regression does not.

Can you explain the benefits of using Lasso for feature selection?

Lasso helps in reducing overfitting, improving model interpretability, and handling high-dimensional data effectively.

What are some limitations of using Lasso for feature selection?

Lasso may struggle with highly correlated features, often selecting one and ignoring others, which can be a drawback.

Are there any alternatives to Lasso for feature selection?

Yes, alternatives include Ridge regression, Elastic Net, and tree-based methods like Random Forest for feature importance.