L0 regularization: Difference between revisions

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 ==Challenges==
-The primary disadvantage of L0 regularization is its computational cost. The optimization problem is NP-hard, meaning finding an optimal solution cannot be done within a reasonable amount of time for large datasets. Furthermore, since it may contain multiple local minima, finding the global minimum can prove challenging.
+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.
-Another potential drawback of L0 regularization is that it may lead to overfitting if not set correctly. If the term is set too high, the model may use too few features and underfit; conversely, if set too low, too many features would be included, leading to overfitting.
 ==Explain Like I'm 5 (ELI5)==
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-[[Category:Terms]] [[Category:Machine learning terms]]
+[[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]]

L0 regularization (view source)