Regularization

Regularization is a technique used in machine learning to prevent models from becoming too complex and fitting noise rather than meaningful patterns—this is called overfitting. It adds a penalty for complexity to the model's objective function, encouraging simpler, more generalizable solutions. Think of it as a way to keep the model from overreacting to the training data’s irregularities, helping it perform better on new, unseen data. Regularization improves the model's ability to make accurate predictions outside of its training set by balancing fit and simplicity.