When aiming to reduce both bias and variance, one might use techniques like ________ to regularize a model.
- Cross-Validation
- Data Augmentation
- Dropout
- L1 Regularization
L1 regularization is a technique used to reduce both bias and variance in a machine learning model. It does so by adding a penalty term to the model's loss function, which encourages the model to use fewer features, thus reducing complexity and variance. Dropout, Cross-Validation, and Data Augmentation are techniques but are not primarily used for regularization.
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