Regularized Least Squares

Regularized Least Squares is a method used to find the best-fitting pattern for data while preventing overfitting. It does this by minimizing the difference between predicted and actual values, and adding a penalty for overly complex models. This penalty encourages simpler, more generalizable solutions. Essentially, it balances closely matching the data with maintaining a model that isn’t too complicated, improving the model's ability to predict new, unseen data accurately. This approach is common in machine learning tasks where data may be noisy or limited.