L0 regularization: Revision history

17 March 2023

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26 February 2023

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25 February 2023

• curprev 14:0314:03, 25 February 2023‎ Alpha5 talk contribs‎ 3,048 bytes +3,048‎ Created page with "{{see also|Machine learning terms}} ==Introduction== L0 regularization, also referred to as the "feature selection" regularization, is a machine learning technique used to encourage models to utilize only some of the available features from data. It does this by adding a penalty term to the loss function that encourages models to have sparse weights - that is, weights close to zero. The goal of L0 regularization is to reduce feature counts used by the model which improve..."