Final answer:
LASSO and Elastic Net are regularization techniques in machine learning. LASSO performs feature selection but can introduce bias, while Elastic Net provides a balance between feature selection and regularization.
Step-by-step explanation:
Pros and Cons of LASSO and Elastic Net:
LASSO (Least Absolute Shrinkage and Selection Operator) and Elastic Net are both popular regularization techniques used in machine learning and statistics to handle high-dimensional data. Here are the pros and cons of each:
LASSO:
Pros: LASSO performs feature selection by shrinking some regression coefficients to zero, which helps in identifying the most important features in a model. It is particularly useful when dealing with high-dimensional data.
Cons: LASSO tends to select only one feature among a group of highly correlated features, which can introduce bias in the selected features. It also struggles when the number of predictors is greater than the number of observations.
Elastic Net:
Pros: Elastic Net combines both LASSO and Ridge regression, providing a balance between feature selection and regularization. It overcomes the limitations of LASSO by allowing the selection of more than one feature in a group of correlated features.
Cons: Elastic Net requires tuning of two parameters - one for L1 penalty (LASSO) and one for L2 penalty (Ridge), which can make the model selection process more complex.
Overall, LASSO and Elastic Net are powerful techniques for handling high-dimensional data, but the choice between them depends on the specific problem and the underlying data characteristics.