How does regularization improve data mining model generalization?

Regularization penalizes model complexity to prevent overfitting.

Regularization adds constraints to model training, typically by penalizing large weights. L1 regularization (Lasso) encourages sparsity, while L2 (Ridge) discourages large parameter values. This improves generalization by reducing variance and preventing memorization of noise in large datasets common in data mining tasks.