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The process of introducing additional information or constraints to a model in order to prevent overfitting, ensuring that the model generalizes well to new, unseen data. Regularization techniques, such as L1 (Lasso) and L2 (Ridge) regularization, add a penalty term to the loss function to reduce the complexity of the model and prevent it from fitting noise in the training data.