Description: Group Lasso is a regularization technique that extends the traditional Lasso method, designed to handle sets of variables that are naturally grouped. Unlike Lasso, which penalizes the sum of the absolute values of individual coefficients, Group Lasso applies a penalty at the group level, meaning that if a group of variables is relevant, they are selected together, while if they are not, they are entirely eliminated. This feature is particularly useful in situations where variables are correlated or have a hierarchical structure, such as in various fields of data analysis, including genetic data analysis or marketing models where similar characteristics are grouped. The technique encourages sparsity at the group level, which not only improves model interpretability but also helps prevent overfitting by reducing model complexity. In summary, Group Lasso is a powerful tool in hyperparameter optimization, allowing analysts and data scientists to build more robust and efficient models by selecting variables in a more coherent and structured manner.
