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When does regularization becomes necessary in Machine Learning?

User Jekennedy
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Final answer:

Regularization is a technique used in Machine Learning to prevent overfitting and improve the generalization ability of a model.

Step-by-step explanation:

Regularization is a technique used in Machine Learning to prevent overfitting and improve the generalization ability of a model.

It becomes necessary when a model is too complex and starts to fit the training data too closely, resulting in poor performance on new, unseen data. Regularization works by adding a penalty term to the loss function that encourages the model to have smaller weights and simpler representations.

For example, in linear regression, regularization can be achieved by adding a regularization term to the ordinary least squares objective function, such as L1 regularization (Lasso) or L2 regularization (Ridge).

User Ramon Dias
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