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L0 regularization: Difference between revisions
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..."
(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...") |
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