Machine Learning & Training

What Is Regularization?

Regularization refers to techniques that add constraints or penalties to a model to prevent it from fitting the training data too closely. By discouraging overly complex solutions, regularization helps a model generalize better to unseen data. Common methods include L1 and L2 penalties, dropout, and early stopping.