L0 regularization: Difference between revisions

Note that L0 regularization is often seen as less practical than other types of regularization, such as [[L1]] or [[L2]], due to its non-convex nature and difficulty optimizing. Furthermore, models regularized with L0 may result in less [[interpretability]] than those regularized with L1 or L2, due to a "winner-takes-all" effect where only a few features are selected for selection.