What are the various regularization techniques used in machine learning?

Question

Accepted Answer

Regularization techniques in machine learning help prevent overfitting by adding a penalty term to the loss function.

L1 regularization (Lasso): Adds the absolute value of the magnitude of coefficients as penalty term.
L2 regularization (Ridge): Adds the square of the magnitude of coefficients as penalty term.
Elastic Net regularization: Combines L1 and L2 regularization to get the best of both worlds.
Dropout regularization: Randomly sets a fraction of input units to zero during training to prevent overfitting.
Early stopping: Stops training when the performance on a validation dataset starts to degrade.
Data augmentation: Increases the size of the training dataset by applying transformations like rotation, scaling, etc.

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