grouplasso
Group Lasso is a statistical method used for feature selection and regularization in linear models. It extends the standard Lasso method by encouraging sparsity at the group level. Instead of selecting individual features, Group Lasso selects entire groups of features to be included or excluded from the model. This is particularly useful when features are naturally organized into meaningful groups, such as all features related to a specific gene or all variables within a particular experimental condition.
The core idea behind Group Lasso is to apply an L1 penalty not to individual coefficients, but
Group Lasso has found applications in various fields, including bioinformatics, where genes are often grouped by