Empirical risk (e.g., mean squared error) Regularizer (e.g., lasso penalty) regularization weight, α ≥ 0 has a complexity penalty that mixes Ridge (L 2 ) and Lasso, thus, aiming to retain benefits of both penalties. While preserving sparsity with Lasso, it tries to proportionally assign nonzero weights to highly-correlated features with Ridge. ] lets manually partition features into groups and controls the complexity with the weighted sum of √ L 2 penalties for each group. It is designed to select or remove the groups of features at a time, rather than considering each feature individually. enforces sparsity both with respect to groups and features within a group by mixing Lasso and Group Lasso. When a particular group is informative but also contains redundant features, it is desirable to filter them out rather than shrink their weights.