Regularization

Regularization is a technique used in machine learning to prevent models from overfitting the training data. Overfitting occurs when a model learns the training data too well, including its noise, which makes it perform poorly on new, unseen data. Regularization adds a penalty to the model's loss function, discouraging overly complex models and promoting simpler, more generalizable solutions.